It has been widely observed that many activation functions and pooling methods of neural network models have ( positive- ) rescaling-invariant property , including ReLU , PReLU , max-pooling , and average pooling , which makes fully-connected neural networks ( FNNs ) and convolutional neural networks ( CNNs ) invariant to ( positive ) rescaling operation across layers . This may cause unneglectable problems with their optimization : ( 1 ) different NN models could be equivalent , but their gradients can be very different from each other ; ( 2 ) it can be proven that the loss functions may have many spurious critical points in the redundant weight space . Even with reduced memory usage , our proposed methods showed reasonable improvement in classification accuracy with 50-layered ResNet . Additionally , with flexibility of our proposed pooling method , we change the reduction rate dynamically every training iteration , and our evaluation results indicated potential regularization effect . We show that principles of sparse coding , which were previously mostly applied to the initial feature detection stages , can also be employed to obtain optimized intermediate complex features . We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy . The approach explicitly enforces symmetry constraints like translation invariance on the feature set . The low-resolution net can roughly locate the volume of interest ( VOI ) from down-sampled CT images , while the full-resolution net and tumor refine net can extract accurate boundaries of kidney and tumor within VOI from full resolution CT images . We innovatively propose dilated convolution blocks ( DCB ) to replace the traditional pooling operations in deeper layers of U-Net architecture to retain detailed semantic information better . Besides , a hybrid loss of dice and weighted cross entropy is used to guide the model to focus on voxels close to the boundary and hard to be distinguished . The goal of this paper is threefold . We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules ( hard and soft vector quantization , sparse coding ) and pooling schemes ( by taking the average , or the maximum ) , which obtains state-of-the-art performance or better on several recognition benchmarks . We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding . In this paper , we consider pooling schemes in which samples are to be tested in two-stages . We show that when batch size is limited as well as pool size , selection schemes tend to be more efficient and flexible . This fingerprint leverages the sparse dictionary learning approaches of the first contribution to map streamlines of subjects to a common space representing prominent bundles . Compact fingerprints are generated by applying a pooling function for each bundle , encoding unique properties of streamlines such as their density along bundles . In a large-scale analysis using data from 861 HCP subjects , the proposed Fiberprint is shown capable of identifying exemplars from the same individual or genetically related subjects , with only a small number of streamlines . We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary . A subject fingerprint is then generated by applying a pooling function for each bundle , thus providing a vector of bundle-wise features describing a particular subject 's white matter geometry . These features encode unique properties of fiber trajectories , such as their density along prominent bundles . These classes introduce the notion of a parallel job as a C++ object , and the public user interfaces are very simple to use . Several examples are proposed for testing the pool operations , and at the end of the chapter an explicit example shows running parallel jobs in parallel in a simple way . Prudent policy design would suggest that a model-based policy should take into account all the rival models . This is achieved as a pooling of the models . The pooling that yields the policy which is robust to model choice is formulated as a constrained min-max problem . It is very important to use the convolutional neural network model for urban short-term traffic congestion forecasting . However , pooling operation process is prone to cause data structure information loss when using CNN model to predict short-term traffic flow . Complete data feature information can not be transmitted , which reduces the prediction ability of the model . In Stage II , the uncertainties are realized and optimal operations for the refinery are determined according to this realization . The resulting large-scale mixed-integer nonlinear programming formulation incorporates integer variables for crude selection and continuous variables for refinery operations , as well as bilinear terms for pooling processes . Nonconvex generalized Benders decomposition is used to solve this problem to obtain an ϵ−global optimum efficiently . We now describe the two stages in detail . subsection : Stage 1 Architecture : Convolutional filtering and pooling The algorithm we apply to extract features from images ( including those with multiple channels ) is summarised in Algorithm . This means that the overall computational cost is dominated by the expensive convolution on the larger grid using operations . One possibility would be to switch to pooling with convolution and therefore resulting in reducing the computational cost by a quarter . However , this creates a representational bottlenecks as the overall dimensionality of the representation drops to resulting in less expressive networks ( see Figure [ reference ] ) . DPM is a shallow model and has limited capability of modeling deformation . While its inference algorithm can be converted to CNNs by treating the distance transform as a special pooling operation , its training is not end - to - end and involves heuristic choices such as selection of components and part sizes . In contrast , deformable ConvNets are deep and perform end - to - end training . If there are active points in the input layer , the cost of building the input hash - table is . For VGG / ResNet / DenseNet networks , assuming the number of active sites reduces by a multiplicative factor with each pooling operation , the cost of building all the hash - tables and rule - books is also , regardless of the depth of the network . section : Experiments While the candidate vectors are passed through a nonlinearity , the gates use an elementwise sigmoid . If the pooling function requires a forget gate and an output gate at each timestep , the full set of computations in the convolutional component is then : where , , and , each in , are the convolutional filter banks and denotes a masked convolution along the timestep dimension . Note that if the filter width is 2 , these equations reduce to the LSTM - like Convolution filters of larger width effectively compute higher - gram features at each timestep ; thus larger widths are especially important for character - level tasks . We use the log - sum - exp ( LSE ) pooling function applied by for semantic segmentation : where is the category , corresponds to the - th stream of FCN , correspond to location in the feature map , is the total number of such elements and is a hyper parameter . The function ’s output is close to average when is small and maximum when is large . Another natural generalization of pooling operations is to allow the pooling operations that are being combined to themselves be learned . Hence in the second approach , we learn to combine pooling filters that are themselves learned . Specifically , the learning is performed within a binary tree ( with number of levels that is pre - specified rather than “ grown ” as in traditional decision trees ) in which each leaf is associated with a learned pooling filter . Then , at query time when comparing two templates and , the similarity score is computed as : . Even when using a carefully engineered fast linear classifier training algorithm , this procedure increases the run time of the pooling procedure . The query time per template comparison is also higher due to the high dimensionality of the input features . Introduction Convolutional Neural Nets ( CNNs ) have been wildly successful for image classification tasks However : They are computationally expensive Any pooling step reduces the dimensionality by at least 4 Previous work suggests using FFTs to compute the convolutional mask – even for small filter sizes – to help with computational time This work suggests using FFTs , and then performing pooling and learning in the Fourier Transform domain Introduced spectral pooling can reduce dimensionality by an user - defined amount ( reduces slower than traditional pooling steps ) Spectral parameterization defines the CNN filters in the frequency domains , which empirically converges 2 - 5 times faster than the standard spatial representation with the same result David Carlson Spectral Pooling July 17 , 2015 2 / 11 Reminder of Fourier Properties Convolution using DFT : We have implemented DM on GPU in the Caffe framework caffe . Using existing Caffe layers like ConvolutionLayer and PoolingLayer , the implementation is straightforward for most layers . We had to specifically code a few layers which are not available in Caffe The network has two processing streams . The residual stream ( blue ) stays at the full image resolution , the pooling stream ( red ) undergoes a sequence of pooling and unpooling operations . The two processing streams are coupled using full - resolution residual units [ reference ] or in combination with 3D scene geometry [ reference ][ reference ][ reference ] . Many of those applications require precise region boundaries For the present work , velocity and pressure fluctuations are used for training to produce the same parameters at the next time instant . Within the autoencoders , different types of methods like average pooling and strided convolutions are tested for the reduction of spatial dimensions . For convolutional neural networks , the physical boundary conditions are implemented in the form of symmetric as well as periodic paddings . In order to conquer the limitation of the fixed size problem , a spatial pyramid pooling ( SPP ) strategy has been proposed in general object recognition and detection . The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation , which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding , linear locality-constrained coding and so on . However , the max pooling strategy in SPP-net only retains the strongest activated pattern , and would completely ignore the frequency : an important signature for identifying different types of images , of the activated patterns . A single-layer SAE is proposed to extract sufficient and appropriate features . A mean-pooling operation is proposed to reduce the dimension of feature vectors . A PSO-based algorithm is employed to optimize the parameters for the SVM classifier . A single-layer SAE is proposed to extract sufficient and appropriate features . A mean-pooling operation is proposed t ... We show how frequency/orientation " columns " as well as topographic filter maps follow naturally from training the model on image pairs . The model also helps explain why square-pooling models yield feature groups with similar grouping properties . Experimental results on synthetic image transformations show that spatially constrained gating is an effective way to reduce the number of parameters and thereby to regularize a transformation-learning model . A uniformity index is defined according to the distribution of localized distortions in one frame , and is utilized to generate a saliency map from the visual attention model , to incorporate temporal memory effect , and to adaptively pool the localized spatio-temporal quality indices . The proposed scheme outperforms the traditional mean-based pooling scheme and shows competitive performance against well-known VQA algorithms when tested on two publicly available video quality databases . To address those issues , we propose a two-stage partial mean pooling ( PMP ) approach to construct compact and discriminative global feature representations . The proposed PMP is meant to tackle the limits of traditional max pooling and mean ( or average ) pooling . By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation , we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets . As fully connected networks can only handle fixed - length inputs , it is a common practice to transform the list of embedding vectors via a pooling layer to get a fixed - length vector : Two most commonly used pooling layers are sum pooling and average pooling , which apply element - wise sum / average operations to the list of embedding vectors . Both embedding and pooling layers operate in a group - wise manner , mapping the original sparse features into multiple fixed - length representation vectors . To evaluate PSPNet , we conduct experiments with several settings , including pooling types of max and average , pooling with just one global feature or four - level features , with and without dimension reduction after the pooling operation and before concatenation . As listed in Table [ reference ] , average pooling works better than max pooling in all settings . Pooling with pyramid parsing outperforms that using global pooling . appendix : Evaluation of off - the - shelf classifiers at higher resolutions In this section , we present some additional classification results using off - the - shelf pretrained classification networks trained with standard average pooling ( ) . As outlined in sec : expand - pooling , sec : expanding - resolution , one of our contributions is a strategy for evaluating classifier networks trained with GeM pooling at scale and exponent at a higher resolution and adapted exponent . After the vehicle image is decomposed into two layers : structure and texture layers , the glow layer is separated from the structure layer to get the background layer . Followed by performing the mean-pooling and the bicubic interpolation algorithm , the atmospheric light map of the background layer can be predicted , meanwhile the transmission of the background layer is estimated through the grayed glow layer , whose gray value is altered by linear mapping . Then , according to the improved atmospheric scattering model , the final restored image can be obtained by fusing the restored background layer and the optimized texture layer . Abstract The generalized pooling problem is involved in many planning and scheduling problems in the petrochemical industry . Compared to the standard pooling problem where the blenders ( or pools ) are not allowed to be connected to one another , the generalized pooling problem has a more complex network structure and allows more types of problem formulations . The state-of-the-art generalized pooling formulations adopt a multi-commodity flow ( MCF ) strategy that was first proposed by Alfaki and Haugland ( 2013a ) and proved to be stronger than the classical p-formulation . Besides , the pooling step has been improved by using two main techniques : the Generalizing Pooling and the spatial pyramid pooling . The Generalizing pooling , that replaces the maxpooling layer commonly used in convolutional neural network , combines pooling operations within a hierarchical tree structure . The Spatial Pyramid Pooling which enables the removal of the fixed size constraint of input image has been conducted . Besides , inspired by the visual system of the human brain , our Boosted Convolutional Layer is designed following a recurrent structure . In addition , rather than using the classical max-pooling , our Boosted Convolutional Neural Network is improved by applying Generalizing pooling which allows pooling to adapt to complex and variable patterns . Furthermore , the Spatial Pyramid Pooling after the last Boosted Block has been conducted after the Boosted Block in order to remove the fixed-size constraint of input image . Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics , such as the bag-of-visual-words (BOV)and its soft- and sparse-coding extensions . We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector -- hence the name Generalized Max Pooling ( GMP ) . It involves equalizing the similarity between each patch and the pooled representation , which is shown to be equivalent to re-weighting the per-patch statistics . The two primary directions lie in ( 1 ) learning a pooling function via ( two strategies of ) combining of max and average pooling , and ( 2 ) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned . In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets ; they are also easy to implement , and can be applied within various deep neural network architectures . The two primary directions lie in : ( 1 ) learning a pooling function via ( two strategies of ) combining of max and average pooling , and ( 2 ) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned . In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets . We then ask “ is there a single global feature pooling variant that ’s most suitable for fine-grained recognition ? ” A thorough evaluation of nine representative pooling algorithms finds that : max pooling outperforms average pooling consistently across models , datasets , and image resolutions ; it does so by reducing the generalization gap ; and generalized pooling ’s performance increases almost monotonically as it changes from average to max . We finally ask : “ Specifically , MWCNN for image restoration is based on U-Net architecture , and inverse wavelet transform ( IWT ) is deployed to reconstruct the high resolution ( HR ) feature maps . The proposed MWCNN can also be viewed as an improvement of dilated filter and a generalization of average pooling and can be applied to not only image restoration tasks , but also any CNNs requiring a pooling operation . The experimental results demonstrate the effectiveness of the proposed MWCNN for tasks , such as image denoising , single image super-resolution , JPEG image artifacts removal , and object classification . We performed parameter tuning in the edge detection problem and it shows that our implementation enables us to obtain better results on the BSDS-500 dataset . As a part of implementations of algorithms , we introduce a generalization of pooling method , which allows using arbitrary structure element . We also analyze the given neural network architectures and show the connections with contemporary approaches . A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class . Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains . We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes . ‘ middle - section ’ ’ of the table shows only small fluctuations in miss - rate ( specially when considering the variance ) . In addition to filter size and layer width , we also experimented with different types of pooling layers ( max - pooling versus mean - pooling ) , see figure 2 of main paper . Other than on the first layer , replacing mean - pooling with max - pooling hurts performance . Self - attention allows slot - specific , robust feature learning . We observe a consistent drop in performance for models that use mean - pooling over the temporal dimension as opposed to self - attention ( Equations ( [ reference ] ) to ( [ reference ] ) ) . It was also employed in to develop second - order convolutional neural networks . Extending current work from covariance pooling to Gaussian pooling would be an interesting direction and should theoretically improve results . bibliography : References Gordo et al . show that fine - tuning these regionally - aggregated representations end - to - end on an external image retrieval dataset using a ranking loss yields significant improvements for instance retrieval . Radenović show that R - MAC pooling is advantageously replaced by a generalized mean pooling ( see sec : p - pooling ) , which is a spatial pooling of the features exponentiated to an exponent over the whole image . This is why the pooling operator tries to favor more locality . Next we discuss the generalized mean pooling operator . Let be the feature tensor computed by a convolutional neural network for a given image , where is the number of feature channels and and are the height and width of the map , respectively . Note that only the GloVe word vectors are used as word representations , as opposed to some models that introduce character - level features . It is also notable that our proposed architecture does not restrict the selection of pooling method ; the performance could further be improved by replacing max - pooling with other advanced algorithms e.g. intra - sentence attention and generalized pooling . subsection : Paraphrase Identification These benefits come with only a light increase in computational overhead during training and a very modest increase in the number of model parameters . Generalizing Pooling Functions in CNNs : Mixed , Gated , and Tree Instead , we removed it and directly output the activation before the removed sigmoid layer . On the other hand , for the loss function network in CLS - GAN , a global mean - pooling layer was added on top of convolutional layers . This produced a feature map that output the conditional loss on different classes . bibliography : References appendix : Testing with Mean - Max Pooling Here , we introduce a new testing technique by using Mean - Max Pooling which can further improve the performance of a well trained CNN in the testing phase without any noticeable computational overhead . The number of filters starts from and is doubled every next block . Following blocks , there is a mean - pooling layer to compress the output feature maps to a feature embedding . The difference between using 4CONV and using ResNet - 12 in our methods is that ResNet - 12 MTL sees the large - scale data training , but 4CONV MTL is learned from scratch because of its poor performance for large - scale data training ( see results in the supplementary ) . Formally , given the word embedding , the hidden state matrices can be obtained as follows : After obtaining the hidden state matrices , the sentiment - word - enhanced sentence representation can be computed as : where denotes the mean - pooling operation towards , is the attention function that calculates the importance of the - th word in the context and indicates the importance of the - th word in the context , and are learnable parameters . Similarly , with the hidden states and for the intensity words and the negation words as attention sources , we can obtain the intensity - word - enhanced sentence representation and the negation - word - enhanced sentence representation . To be more specific , after the log-Gabor filter processing , the local amplitude and phase from the left and right views of the reference and distorted 3D images are utilized as features in local quality evaluation . Meanwhile , the global structure changes of the left and right views are also incorporated into the final quality pooling . The overall 3D quality score is obtained by combining the local and global quality indexes together . The most knowing architectures with limited flexibility such as chaining fail against such asymmetry . We propose a new architecture referred to as single pooling with only two skills per agent and we demonstrate its efficiency under various situations of asymmetry . In the second part , we focus on routing problems in multi‐channel call centers . Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules named cascade corner pooling and center pooling , which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions , respectively . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , which outperforms all existing one-stage detectors by a large margin . Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules , cascade corner pooling , and center pooling , that enrich information collected by both the top-left and bottom-right corners and provide more recognizable information from the central regions . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , outperforming all existing one-stage detectors by at least 4.9 % . In this work , we present a multi-resolution dense encoder and stack decoder network to deal with this problem . On the one hand , the dense encoder embeds shallow detailed feature into deep semantic feature through proposed information-reserved down-sampling method called CE-Pooling . On the other hand , the stack decoder gradually enhances the detailed feature through iterative attention fusion . For agents with constant risk aversion , we demonstrate that the aggregate probability exhibits several desirable properties , and is related to independently motivated techniques . We argue that the market-based approach provides a plausible mechanism for belief aggregation in multiagent systems , as it directly addresses self-motivated agent incentives for participation and for truthfulness , and can provide a decision-theoretic foundation for the " expert weights " often employed in centralized pooling techniques . This paper examines the panel data models when the regression coefficients are fixed , random , and mixed , and proposed the different estimators for this model . We used the Mote Carlo simulation for making comparisons between the behavior of several estimation methods , such as Random Coefficient Regression ( RCR ) , Classical Pooling ( CP ) , and Mean Group ( MG ) estimators , in the three cases for regression coefficients . The Monte Carlo simulation results suggest that the RCR estimators perform well in small samples if the coefficients are random . This paper introduces a novel concept for Remote Radio Head ( RRH ) powering : PoF pooling , and its application for Multi-Core Fiber ( MCF ) enabled by SDN . Simulations using stochastic geometry prove the benefits of this approach in a phantom cell scenario . Next , we design a novel task splitting mechanism to reduce the training time without decreasing the accuracy of the original method . To further improve the performance of ForestLayer , we propose three system-level optimization techniques , including lazy scan , pre-pooling , and partial transmission . Besides the systematic optimization , we also propose a set of high-level programming APIs to improve the ease-of-use of ForestLayer . Providing variable semi-local receptive fields and weights , the skeleton serves as a bridge that connect local convolutional feature extractors and a global recurrent feature integrator . Experimental results on indoor scene datasets show advantages of the proposed approach compared to state-of-the-art architectures that adopt static pooling methods . We review the approach to set-based analysis through offsets developed by Weinberg and Umbach in a recent correction to their original paper . We propose a method for variance estimation under this design and use simulations and a real-data example to illustrate the precision benefits of (y , c)-pooling relative to y-pooling . We also note and illustrate that set-based models permit estimation of covariate interactions with exposure . The number 28 28 inception modules is increased from 2 to 3 . Inside the modules , sometimes average , sometimes maximum - pooling is employed . This is indicated in the entries corresponding to the pooling layers of the table . Which pooling method is better ? We compare maximum pooling , average pooling and LSE pooling methods to train proposal network with image - level supervision . Table [ reference ] lists the classification and localization performance of the three different pooling methods . In our experiments , we fed the node embeddings to a fully - connected neural network and applied each pooling method element - wise . We found no significant performance difference across the three different pooling approaches ; we thus adopt the max - pooling method as our default pooling approach . Node - based Graph Embedding . AlexNet is the eight - layer that won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) in 2012 by a large margin . It employs five convolutional layers with optional pooling and local response normalization . These are then followed by three fully - connected layers and a softmax classifier ( cf . Such a network with learnable pooling function is referred to as a fully trainable network ( FTN ) . Experimental results demonstrate that the proposed RNN based pooling can well approximate the existing pooling functions with just one neuron , thus making it appropriate to be used as pooling function in a network with its rich representation capability . Furthermore , experiments have shown that the proposed FTN can achieve better performance than the existing pooling methods under similar network architectures for image classification . Heuristically , we construct a fault diagnosis framework called adaptive overlapping CNN ( AOCNN ) to deal with one dimension ( 1D ) raw vibration signals directly . The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square ( RMS ) pooling layer , and the marginal problem embedded in the CNN is relieved by employing the overlapping layer . Meanwhile , the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage .
It has been widely observed that many activation functions and pooling methods of neural network models have ( positive- ) rescaling-invariant property , including ReLU , PReLU , max-pooling , and average pooling , which makes fully-connected neural networks ( FNNs ) and convolutional neural networks ( CNNs ) invariant to ( positive ) rescaling operation across layers . This may cause unneglectable problems with their optimization : ( 1 ) different NN models could be equivalent , but their gradients can be very different from each other ; ( 2 ) it can be proven that the loss functions may have many spurious critical points in the redundant weight space .
Even with reduced memory usage , our proposed methods showed reasonable improvement in classification accuracy with 50-layered ResNet . Additionally , with flexibility of our proposed pooling method , we change the reduction rate dynamically every training iteration , and our evaluation results indicated potential regularization effect .
We show that principles of sparse coding , which were previously mostly applied to the initial feature detection stages , can also be employed to obtain optimized intermediate complex features . We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy . The approach explicitly enforces symmetry constraints like translation invariance on the feature set .
The low-resolution net can roughly locate the volume of interest ( VOI ) from down-sampled CT images , while the full-resolution net and tumor refine net can extract accurate boundaries of kidney and tumor within VOI from full resolution CT images . We innovatively propose dilated convolution blocks ( DCB ) to replace the traditional pooling operations in deeper layers of U-Net architecture to retain detailed semantic information better . Besides , a hybrid loss of dice and weighted cross entropy is used to guide the model to focus on voxels close to the boundary and hard to be distinguished .
The goal of this paper is threefold . We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules ( hard and soft vector quantization , sparse coding ) and pooling schemes ( by taking the average , or the maximum ) , which obtains state-of-the-art performance or better on several recognition benchmarks . We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding .
In this paper , we consider pooling schemes in which samples are to be tested in two-stages . We show that when batch size is limited as well as pool size , selection schemes tend to be more efficient and flexible .
This fingerprint leverages the sparse dictionary learning approaches of the first contribution to map streamlines of subjects to a common space representing prominent bundles . Compact fingerprints are generated by applying a pooling function for each bundle , encoding unique properties of streamlines such as their density along bundles . In a large-scale analysis using data from 861 HCP subjects , the proposed Fiberprint is shown capable of identifying exemplars from the same individual or genetically related subjects , with only a small number of streamlines .
We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary . A subject fingerprint is then generated by applying a pooling function for each bundle , thus providing a vector of bundle-wise features describing a particular subject 's white matter geometry . These features encode unique properties of fiber trajectories , such as their density along prominent bundles .
These classes introduce the notion of a parallel job as a C++ object , and the public user interfaces are very simple to use . Several examples are proposed for testing the pool operations , and at the end of the chapter an explicit example shows running parallel jobs in parallel in a simple way .
Prudent policy design would suggest that a model-based policy should take into account all the rival models . This is achieved as a pooling of the models . The pooling that yields the policy which is robust to model choice is formulated as a constrained min-max problem .
It is very important to use the convolutional neural network model for urban short-term traffic congestion forecasting . However , pooling operation process is prone to cause data structure information loss when using CNN model to predict short-term traffic flow . Complete data feature information can not be transmitted , which reduces the prediction ability of the model .
In Stage II , the uncertainties are realized and optimal operations for the refinery are determined according to this realization . The resulting large-scale mixed-integer nonlinear programming formulation incorporates integer variables for crude selection and continuous variables for refinery operations , as well as bilinear terms for pooling processes . Nonconvex generalized Benders decomposition is used to solve this problem to obtain an ϵ−global optimum efficiently .
We now describe the two stages in detail . subsection : Stage 1 Architecture : Convolutional filtering and pooling The algorithm we apply to extract features from images ( including those with multiple channels ) is summarised in Algorithm .
This means that the overall computational cost is dominated by the expensive convolution on the larger grid using operations . One possibility would be to switch to pooling with convolution and therefore resulting in reducing the computational cost by a quarter . However , this creates a representational bottlenecks as the overall dimensionality of the representation drops to resulting in less expressive networks ( see Figure [ reference ] ) .
DPM is a shallow model and has limited capability of modeling deformation . While its inference algorithm can be converted to CNNs by treating the distance transform as a special pooling operation , its training is not end - to - end and involves heuristic choices such as selection of components and part sizes . In contrast , deformable ConvNets are deep and perform end - to - end training .
If there are active points in the input layer , the cost of building the input hash - table is . For VGG / ResNet / DenseNet networks , assuming the number of active sites reduces by a multiplicative factor with each pooling operation , the cost of building all the hash - tables and rule - books is also , regardless of the depth of the network . section : Experiments
While the candidate vectors are passed through a nonlinearity , the gates use an elementwise sigmoid . If the pooling function requires a forget gate and an output gate at each timestep , the full set of computations in the convolutional component is then : where , , and , each in , are the convolutional filter banks and denotes a masked convolution along the timestep dimension . Note that if the filter width is 2 , these equations reduce to the LSTM - like Convolution filters of larger width effectively compute higher - gram features at each timestep ; thus larger widths are especially important for character - level tasks .
We use the log - sum - exp ( LSE ) pooling function applied by for semantic segmentation : where is the category , corresponds to the - th stream of FCN , correspond to location in the feature map , is the total number of such elements and is a hyper parameter . The function ’s output is close to average when is small and maximum when is large .
Another natural generalization of pooling operations is to allow the pooling operations that are being combined to themselves be learned . Hence in the second approach , we learn to combine pooling filters that are themselves learned . Specifically , the learning is performed within a binary tree ( with number of levels that is pre - specified rather than “ grown ” as in traditional decision trees ) in which each leaf is associated with a learned pooling filter .
Then , at query time when comparing two templates and , the similarity score is computed as : . Even when using a carefully engineered fast linear classifier training algorithm , this procedure increases the run time of the pooling procedure . The query time per template comparison is also higher due to the high dimensionality of the input features .
Introduction Convolutional Neural Nets ( CNNs ) have been wildly successful for image classification tasks However : They are computationally expensive Any pooling step reduces the dimensionality by at least 4 Previous work suggests using FFTs to compute the convolutional mask – even for small filter sizes – to help with computational time This work suggests using FFTs , and then performing pooling and learning in the Fourier Transform domain Introduced spectral pooling can reduce dimensionality by an user - defined amount ( reduces slower than traditional pooling steps ) Spectral parameterization defines the CNN filters in the frequency domains , which empirically converges 2 - 5 times faster than the standard spatial representation with the same result David Carlson Spectral Pooling July 17 , 2015 2 / 11 Reminder of Fourier Properties Convolution using DFT :
We have implemented DM on GPU in the Caffe framework caffe . Using existing Caffe layers like ConvolutionLayer and PoolingLayer , the implementation is straightforward for most layers . We had to specifically code a few layers which are not available in Caffe
The network has two processing streams . The residual stream ( blue ) stays at the full image resolution , the pooling stream ( red ) undergoes a sequence of pooling and unpooling operations . The two processing streams are coupled using full - resolution residual units [ reference ] or in combination with 3D scene geometry [ reference ][ reference ][ reference ] . Many of those applications require precise region boundaries
For the present work , velocity and pressure fluctuations are used for training to produce the same parameters at the next time instant . Within the autoencoders , different types of methods like average pooling and strided convolutions are tested for the reduction of spatial dimensions . For convolutional neural networks , the physical boundary conditions are implemented in the form of symmetric as well as periodic paddings .
In order to conquer the limitation of the fixed size problem , a spatial pyramid pooling ( SPP ) strategy has been proposed in general object recognition and detection . The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation , which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding , linear locality-constrained coding and so on . However , the max pooling strategy in SPP-net only retains the strongest activated pattern , and would completely ignore the frequency : an important signature for identifying different types of images , of the activated patterns .
A single-layer SAE is proposed to extract sufficient and appropriate features . A mean-pooling operation is proposed to reduce the dimension of feature vectors . A PSO-based algorithm is employed to optimize the parameters for the SVM classifier .
A single-layer SAE is proposed to extract sufficient and appropriate features . A mean-pooling operation is proposed t ...
We show how frequency/orientation " columns " as well as topographic filter maps follow naturally from training the model on image pairs . The model also helps explain why square-pooling models yield feature groups with similar grouping properties . Experimental results on synthetic image transformations show that spatially constrained gating is an effective way to reduce the number of parameters and thereby to regularize a transformation-learning model .
A uniformity index is defined according to the distribution of localized distortions in one frame , and is utilized to generate a saliency map from the visual attention model , to incorporate temporal memory effect , and to adaptively pool the localized spatio-temporal quality indices . The proposed scheme outperforms the traditional mean-based pooling scheme and shows competitive performance against well-known VQA algorithms when tested on two publicly available video quality databases .
To address those issues , we propose a two-stage partial mean pooling ( PMP ) approach to construct compact and discriminative global feature representations . The proposed PMP is meant to tackle the limits of traditional max pooling and mean ( or average ) pooling . By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation , we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets .
As fully connected networks can only handle fixed - length inputs , it is a common practice to transform the list of embedding vectors via a pooling layer to get a fixed - length vector : Two most commonly used pooling layers are sum pooling and average pooling , which apply element - wise sum / average operations to the list of embedding vectors . Both embedding and pooling layers operate in a group - wise manner , mapping the original sparse features into multiple fixed - length representation vectors .
To evaluate PSPNet , we conduct experiments with several settings , including pooling types of max and average , pooling with just one global feature or four - level features , with and without dimension reduction after the pooling operation and before concatenation . As listed in Table [ reference ] , average pooling works better than max pooling in all settings . Pooling with pyramid parsing outperforms that using global pooling .
appendix : Evaluation of off - the - shelf classifiers at higher resolutions In this section , we present some additional classification results using off - the - shelf pretrained classification networks trained with standard average pooling ( ) . As outlined in sec : expand - pooling , sec : expanding - resolution , one of our contributions is a strategy for evaluating classifier networks trained with GeM pooling at scale and exponent at a higher resolution and adapted exponent .
After the vehicle image is decomposed into two layers : structure and texture layers , the glow layer is separated from the structure layer to get the background layer . Followed by performing the mean-pooling and the bicubic interpolation algorithm , the atmospheric light map of the background layer can be predicted , meanwhile the transmission of the background layer is estimated through the grayed glow layer , whose gray value is altered by linear mapping . Then , according to the improved atmospheric scattering model , the final restored image can be obtained by fusing the restored background layer and the optimized texture layer .
Abstract The generalized pooling problem is involved in many planning and scheduling problems in the petrochemical industry . Compared to the standard pooling problem where the blenders ( or pools ) are not allowed to be connected to one another , the generalized pooling problem has a more complex network structure and allows more types of problem formulations . The state-of-the-art generalized pooling formulations adopt a multi-commodity flow ( MCF ) strategy that was first proposed by Alfaki and Haugland ( 2013a ) and proved to be stronger than the classical p-formulation .
Besides , the pooling step has been improved by using two main techniques : the Generalizing Pooling and the spatial pyramid pooling . The Generalizing pooling , that replaces the maxpooling layer commonly used in convolutional neural network , combines pooling operations within a hierarchical tree structure . The Spatial Pyramid Pooling which enables the removal of the fixed size constraint of input image has been conducted .
Besides , inspired by the visual system of the human brain , our Boosted Convolutional Layer is designed following a recurrent structure . In addition , rather than using the classical max-pooling , our Boosted Convolutional Neural Network is improved by applying Generalizing pooling which allows pooling to adapt to complex and variable patterns . Furthermore , the Spatial Pyramid Pooling after the last Boosted Block has been conducted after the Boosted Block in order to remove the fixed-size constraint of input image .
Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics , such as the bag-of-visual-words (BOV)and its soft- and sparse-coding extensions . We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector -- hence the name Generalized Max Pooling ( GMP ) . It involves equalizing the similarity between each patch and the pooled representation , which is shown to be equivalent to re-weighting the per-patch statistics .
The two primary directions lie in ( 1 ) learning a pooling function via ( two strategies of ) combining of max and average pooling , and ( 2 ) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned . In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets ; they are also easy to implement , and can be applied within various deep neural network architectures .
The two primary directions lie in : ( 1 ) learning a pooling function via ( two strategies of ) combining of max and average pooling , and ( 2 ) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned . In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling . We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets .
We then ask “ is there a single global feature pooling variant that ’s most suitable for fine-grained recognition ? ” A thorough evaluation of nine representative pooling algorithms finds that : max pooling outperforms average pooling consistently across models , datasets , and image resolutions ; it does so by reducing the generalization gap ; and generalized pooling ’s performance increases almost monotonically as it changes from average to max . We finally ask : “
Specifically , MWCNN for image restoration is based on U-Net architecture , and inverse wavelet transform ( IWT ) is deployed to reconstruct the high resolution ( HR ) feature maps . The proposed MWCNN can also be viewed as an improvement of dilated filter and a generalization of average pooling and can be applied to not only image restoration tasks , but also any CNNs requiring a pooling operation . The experimental results demonstrate the effectiveness of the proposed MWCNN for tasks , such as image denoising , single image super-resolution , JPEG image artifacts removal , and object classification .
We performed parameter tuning in the edge detection problem and it shows that our implementation enables us to obtain better results on the BSDS-500 dataset . As a part of implementations of algorithms , we introduce a generalization of pooling method , which allows using arbitrary structure element . We also analyze the given neural network architectures and show the connections with contemporary approaches .
A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class . Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains . We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes .
‘ middle - section ’ ’ of the table shows only small fluctuations in miss - rate ( specially when considering the variance ) . In addition to filter size and layer width , we also experimented with different types of pooling layers ( max - pooling versus mean - pooling ) , see figure 2 of main paper . Other than on the first layer , replacing mean - pooling with max - pooling hurts performance .
Self - attention allows slot - specific , robust feature learning . We observe a consistent drop in performance for models that use mean - pooling over the temporal dimension as opposed to self - attention ( Equations ( [ reference ] ) to ( [ reference ] ) ) .
It was also employed in to develop second - order convolutional neural networks . Extending current work from covariance pooling to Gaussian pooling would be an interesting direction and should theoretically improve results . bibliography : References
Gordo et al . show that fine - tuning these regionally - aggregated representations end - to - end on an external image retrieval dataset using a ranking loss yields significant improvements for instance retrieval . Radenović show that R - MAC pooling is advantageously replaced by a generalized mean pooling ( see sec : p - pooling ) , which is a spatial pooling of the features exponentiated to an exponent over the whole image .
This is why the pooling operator tries to favor more locality . Next we discuss the generalized mean pooling operator . Let be the feature tensor computed by a convolutional neural network for a given image , where is the number of feature channels and and are the height and width of the map , respectively .
Note that only the GloVe word vectors are used as word representations , as opposed to some models that introduce character - level features . It is also notable that our proposed architecture does not restrict the selection of pooling method ; the performance could further be improved by replacing max - pooling with other advanced algorithms e.g. intra - sentence attention and generalized pooling . subsection : Paraphrase Identification
These benefits come with only a light increase in computational overhead during training and a very modest increase in the number of model parameters . Generalizing Pooling Functions in CNNs : Mixed , Gated , and Tree
Instead , we removed it and directly output the activation before the removed sigmoid layer . On the other hand , for the loss function network in CLS - GAN , a global mean - pooling layer was added on top of convolutional layers . This produced a feature map that output the conditional loss on different classes .
bibliography : References appendix : Testing with Mean - Max Pooling Here , we introduce a new testing technique by using Mean - Max Pooling which can further improve the performance of a well trained CNN in the testing phase without any noticeable computational overhead .
The number of filters starts from and is doubled every next block . Following blocks , there is a mean - pooling layer to compress the output feature maps to a feature embedding . The difference between using 4CONV and using ResNet - 12 in our methods is that ResNet - 12 MTL sees the large - scale data training , but 4CONV MTL is learned from scratch because of its poor performance for large - scale data training ( see results in the supplementary ) .
Formally , given the word embedding , the hidden state matrices can be obtained as follows : After obtaining the hidden state matrices , the sentiment - word - enhanced sentence representation can be computed as : where denotes the mean - pooling operation towards , is the attention function that calculates the importance of the - th word in the context and indicates the importance of the - th word in the context , and are learnable parameters . Similarly , with the hidden states and for the intensity words and the negation words as attention sources , we can obtain the intensity - word - enhanced sentence representation and the negation - word - enhanced sentence representation .
To be more specific , after the log-Gabor filter processing , the local amplitude and phase from the left and right views of the reference and distorted 3D images are utilized as features in local quality evaluation . Meanwhile , the global structure changes of the left and right views are also incorporated into the final quality pooling . The overall 3D quality score is obtained by combining the local and global quality indexes together .
The most knowing architectures with limited flexibility such as chaining fail against such asymmetry . We propose a new architecture referred to as single pooling with only two skills per agent and we demonstrate its efficiency under various situations of asymmetry . In the second part , we focus on routing problems in multi‐channel call centers .
Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules named cascade corner pooling and center pooling , which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions , respectively . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , which outperforms all existing one-stage detectors by a large margin .
Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules , cascade corner pooling , and center pooling , that enrich information collected by both the top-left and bottom-right corners and provide more recognizable information from the central regions . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , outperforming all existing one-stage detectors by at least 4.9 % .
In this work , we present a multi-resolution dense encoder and stack decoder network to deal with this problem . On the one hand , the dense encoder embeds shallow detailed feature into deep semantic feature through proposed information-reserved down-sampling method called CE-Pooling . On the other hand , the stack decoder gradually enhances the detailed feature through iterative attention fusion .
For agents with constant risk aversion , we demonstrate that the aggregate probability exhibits several desirable properties , and is related to independently motivated techniques . We argue that the market-based approach provides a plausible mechanism for belief aggregation in multiagent systems , as it directly addresses self-motivated agent incentives for participation and for truthfulness , and can provide a decision-theoretic foundation for the " expert weights " often employed in centralized pooling techniques .
This paper examines the panel data models when the regression coefficients are fixed , random , and mixed , and proposed the different estimators for this model . We used the Mote Carlo simulation for making comparisons between the behavior of several estimation methods , such as Random Coefficient Regression ( RCR ) , Classical Pooling ( CP ) , and Mean Group ( MG ) estimators , in the three cases for regression coefficients . The Monte Carlo simulation results suggest that the RCR estimators perform well in small samples if the coefficients are random .
This paper introduces a novel concept for Remote Radio Head ( RRH ) powering : PoF pooling , and its application for Multi-Core Fiber ( MCF ) enabled by SDN . Simulations using stochastic geometry prove the benefits of this approach in a phantom cell scenario .
Next , we design a novel task splitting mechanism to reduce the training time without decreasing the accuracy of the original method . To further improve the performance of ForestLayer , we propose three system-level optimization techniques , including lazy scan , pre-pooling , and partial transmission . Besides the systematic optimization , we also propose a set of high-level programming APIs to improve the ease-of-use of ForestLayer .
Providing variable semi-local receptive fields and weights , the skeleton serves as a bridge that connect local convolutional feature extractors and a global recurrent feature integrator . Experimental results on indoor scene datasets show advantages of the proposed approach compared to state-of-the-art architectures that adopt static pooling methods .
We review the approach to set-based analysis through offsets developed by Weinberg and Umbach in a recent correction to their original paper . We propose a method for variance estimation under this design and use simulations and a real-data example to illustrate the precision benefits of (y , c)-pooling relative to y-pooling . We also note and illustrate that set-based models permit estimation of covariate interactions with exposure .
The number 28 28 inception modules is increased from 2 to 3 . Inside the modules , sometimes average , sometimes maximum - pooling is employed . This is indicated in the entries corresponding to the pooling layers of the table .
Which pooling method is better ? We compare maximum pooling , average pooling and LSE pooling methods to train proposal network with image - level supervision . Table [ reference ] lists the classification and localization performance of the three different pooling methods .
In our experiments , we fed the node embeddings to a fully - connected neural network and applied each pooling method element - wise . We found no significant performance difference across the three different pooling approaches ; we thus adopt the max - pooling method as our default pooling approach . Node - based Graph Embedding .
AlexNet is the eight - layer that won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) in 2012 by a large margin . It employs five convolutional layers with optional pooling and local response normalization . These are then followed by three fully - connected layers and a softmax classifier ( cf .
Such a network with learnable pooling function is referred to as a fully trainable network ( FTN ) . Experimental results demonstrate that the proposed RNN based pooling can well approximate the existing pooling functions with just one neuron , thus making it appropriate to be used as pooling function in a network with its rich representation capability . Furthermore , experiments have shown that the proposed FTN can achieve better performance than the existing pooling methods under similar network architectures for image classification .
Heuristically , we construct a fault diagnosis framework called adaptive overlapping CNN ( AOCNN ) to deal with one dimension ( 1D ) raw vibration signals directly . The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square ( RMS ) pooling layer , and the marginal problem embedded in the CNN is relieved by employing the overlapping layer . Meanwhile , the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage .
Optimizing Neural Networks in the Equivalent Class Space Bi-linearly weighted fractional max pooling Learning Optimized Features for Hierarchical Models of Invariant Object Recognition A Triple-Stage Self-Guided Network for Kidney Tumor Segmentation Learning mid-level features for recognition Two-stage testing using selection schemes. Data-driven methods for characterizing individual differences in brain MRI Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis Further Thread Pools A constrained min‐max algorithm for rival models of the same economic system Dilated-Dense Network for traffic congestion prediction Model Integrated crude selection and refinery optimization under uncertainty Enhanced image classification with a fast-learning shallow convolutional neural network Rethinking the Inception Architecture for Computer Vision Deformable Convolutional Networks Submanifold Sparse Convolutional Networks Quasi-Recurrent Neural Networks ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Triplet probabilistic embedding for face verification and clustering Spectral Representations for Convolutional Neural Networks DeepMatching: Hierarchical Deformable Dense Matching Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes Development of Deep Learning Methods for Inflow Turbulence Generation. HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs Scene classification based on single-layer SAE and SVM Scene classification based on single-layer SAE and SVM Feature grouping from spatially constrained multiplicative interaction Spatio-temporal quality pooling adaptive to distortion distribution and visual attention Two-stage pooling of deep convolutional features for image retrieval Deep Interest Network for Click-Through Rate Prediction Pyramid Scene Parsing Network MultiGrain: a unified image embedding for classes and instances Vehicle license plate recognition in dense fog based on improved atmospheric scattering model New Multi-Commodity Flow Formulations for the Generalized Pooling Problem Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition Boosted Convolutional Neural Network for object recognition at large scale Generalized Max Pooling Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree On Global Feature Pooling for Fine-grained Visual Categorization Multi-Level Wavelet Convolutional Neural Networks On differentiability of common image processing algorithms Hierarchical semantic parsing for object pose estimation in densely cluttered scenes Taking a deeper look at pedestrians Global-Locally Self-Attentive Dialogue State Tracker Covariance Pooling for Facial Expression Recognition MultiGrain: a unified image embedding for classes and instances Cell-aware Stacked LSTMs for Modeling Sentences Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Dual Path Networks Meta-Transfer Learning for Few-Shot Learning A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Perceptual quality assessment of stereoscopic images based on local and global visual characteristics OPTIMIZATION OF MULTI-CHANNEL AND MULTI- SKILL CALL CENTERS CenterNet: Object Detection with Keypoint Triplets CenterNet: Keypoint Triplets for Object Detection DSNet:Multi-resolution Dense Encoder and Stack Decoder Network for Aerial Image Segmentation Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market A Comparative Study for Estimation Parameters in Panel Data Model SDN-Based Multi-Core Power-Over-Fiber (PoF) System for 5G Fronthaul: Towards PoF Pooling ForestLayer: Efficient training of deep forests on distributed task-parallel platforms DAR-Net: Dynamic Aggregation Network for Semantic Scene Segmentation An efficient design strategy for logistic regression using outcome- and covariate-dependent pooling of biospecimens prior to assay. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification A Fully Trainable Network with RNN-based Pooling An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network
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ISBI CVPR 2015 International Joint Conference on Neural Networks (IJCNN) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AISTATS 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) NIPS International Journal of Computer Vision 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) LABELS/DLMIA@MICCAI VCIP ICIP KDD 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv SMC ICRA 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACL 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ArXiv ACML AISTATS International Journal of Computer Vision NIPS 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ACL ICML 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
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