Then we divide the service time into two independent parts and derive distributions of these two parts respectively in a recursive way . With the convolution technique we obtain the service time distribution of every queuing system . Sample numerical example is presented at the end . The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates . Facing the challenge of the classification task , we propose a novel 3D convolution neural network ( CNN ) to reduce false positives in lung nodule detection . The novel 3D CNN includes embedded multiple branches in its structure . In this demonstration , we will reveal a full functional wireless access for vehicular environments ( WAVE ) prototype , which achieves the complete functions defined by the IEEE 802.11p standard through integrating effective and novel base-band algorithms including fast time-synchronization , multiple-stage multiple Doppler detection , accurate channel estimate , and joint QAM demodulation and convolution decoding , all upon the Cyclone III field programmable gate array ( FPGA ) development kit from the Terasic Company . The WAVE prototype has been extensively tested under various road conditions showing satisfied performances . Using the simulated annealing arithmetic we predigested the energy function of the neural network . On the basis of the trellis diagram of the convolution codes and the new energy function of the neural network , a new neural network decoding method of the convolution decoding is presented . The simulation result shows that the network can always find global optimum . A quantitative SPECT ( single photon emission computed tomography ) regime based on simultaneous acquisition of emission and transmission data has been developed and validated . Transmission data are acquired using a scanning collimated line source and incorporated into a modified convolution-subtraction scatter correction method which produces transmission-dependent scatter fractions . This method tailors scatter fraction to attenuation path length , falling to zero outside the body . Here , we propose a mass detection CAD system that is based on Faster R-CNN . First , we applied a novel convolution network in the backbone of Faster R-CNN , namely deformable convolution network ( DCN ) , which improves the detection of lesions with varying shapes and sizes . Second , the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map . However , the detection result of images with complex background is unsatisfactory , so as images with dense and small objects . Aiming at these problems , we propose a method that combined Feature Pyramid Network(FPN ) and Deformable Convolution Network(DCN ) to improve the Faster RCNN framework , which helps to improve the detection result . The improved network combines the low-level structural information and the high-level semantic information together to enhance the feature representation . In this paper , a method for determining grasping position and angle of sea cucumber by rotatable bounding box is proposed . Firstly , an improved full convolution image segmentation network model is proposed . Secondly , the expanded sea cucumber image is used to train the improved full convolution neural network . Thirdly , we use the trained neural network to segment the image to get the sea cucumber target . Finally , the grasping position and angle of sea cucumber are determined according to the minimum enclosing rectangle of the largest connected region of the segmented image . Experiments show that the improved full convolution image segmentation network model can effectively improve the accuracy and speed of sea cucumber target segmentation . The proposed method can determine the grasping position and angle of sea cucumber at any rotation angle in real time and accurately . The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution . Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters . This means that no information about motion needs to be transmitted to the decoder . The irregular sampling of the prediction , due to motion estimates with sub-pixel accuracy , is dealt with using a new method , continuous normalized convolution . Image values on an interlaced sampling grid are estimated from the irregularly sampled predictions using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the integration filter . The challenge is caused by significant variability of eye appearance in illumination , shape , color , viewing angle and dynamics , and computation related issues . In this paper , we propose a convolution-based means of gradient method to efficiently and accurately locate the eye center in low resolution images . Priority of enhancing its computation is achieved by the use of FFT transform and fewer identified pixels of circular boundary of potential eye centres . The principal idea of obtaining optimal conditions is locally adjoint mappings . Then , applying infimal convolution concept of convex functions , step by step , we construct the dual problems for third-order discrete , discrete-approximate and differential inclusions and prove duality results . It appears that the Euler – Lagrange-type inclusions are duality relations for both primary and dual problems and that the dual problem for discrete-approximate problem makes a bridge between the dual problems of discrete and continuous problems . Our sDropout randomly splits the data into two subsets and keeps both rather than discards one subset . We also introduce two rotational convolution techniques , i.e. rotate-pooling convolution ( RPC ) and flip-rotate-pooling convolution ( FRPC ) to boost CNNs ' performance on the robustness for rotation transformation . These two techniques encode rotation invariance into the network without adding extra parameters . In this example , consists of a sequence of layers : Conv - BN - ReLU - Conv - BN , where Conv and BN stands for Convolution and Batch Normalization respectively . This construction scheme is adopted in all our experiments while reproducing the results of . The unit first concatenates the two incoming streams by using a pooling layer in order to reduce the size of the residual stream . Then the concatenated features are fed through two convolution units . Each convolution unit consists of a 3 × 3 convolution layer followed by a batch normalization layer [ reference ] and a ReLU activation function . As illustrated in Figure [ reference ] , it takes as input the embedding of words ( often trained beforehand with unsupervised methods ) in the sentence aligned sequentially , and summarize the meaning of a sentence through layers of convolution and pooling , until reaching a fixed length vectorial representation in the final layer . As in most convolutional models , we use convolution units with a local “ receptive field ” and shared weights , but we design a large feature map to adequately model the rich structures in the composition of words . paragraph : Convolution As explained before , this technique presents two main drawbacks : reduction of segmentation accuracy and low efficiency . An alternative approach , known as fully CNNs ( FCNNs ) , mitigates these limitations by considering the network as a single non - linear convolution , which is trained in an end - to - end fashion . An important advantage of FCNNs , compared to standard CNNs , is that they can be applied to images of arbitrary size . However , recent attempts to benefit from temporal correlations in video super - resolution have been limited to naive or inefficient architectures . In this paper , we introduce spatio - temporal sub - pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real - time speed . Specifically , we discuss the use of early fusion , slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames . , “ Im - ” ) , we design a coupled word embedding learned from character - level embedding and word - level embedding . We first design a character - level convolution neural network ( Char - CNN ) to obtain character - level embedding . Different from , the designed Char - CNN is a fully convolutional network without max - pooling layer to capture better semantic information in character chunk . A middle ground of directly connecting the highest convolutional features to the input and output respectively of the generator and discriminator worked well . The first layer of the GAN , which takes a uniform noise distribution as input , could be called fully connected as it is just a matrix multiplication , but the result is reshaped into a 4 - dimensional tensor and used as the start of the convolution stack . For the discriminator , the last convolution layer is flattened and then fed into a single sigmoid output . This restriction was later alleviated in by introducing modern practices for recurrent neural network training to the original graph neural network framework . introduced a convolution - like propagation rule on graphs and methods for graph - level classification . Their approach requires to learn node degree - specific weight matrices which does not scale to large graphs with wide node degree distributions . The predictor outputs a shape as an N × 3 matrix M , each row containing the coordinates of one point . The encoder is a composition of convolution and ReLU layers ; in addition , a random vector r is subsumed so that it perturbs the prediction from the image I. This leads to predictions of object classes at different positions which can then simply be averaged over the whole image . This scheme was first described by Lin_2014 and further regularizes the network as the one by one convolution has much less parameters than a fully connected layer . Overall our architecture is thus reduced to consist only of convolutional layers with rectified linear non - linearities and an averaging + softmax layer to produce predictions over the whole image . In this paper , we proposed an effective and efficient method for accurate vessel segmentation in color fundus images using encoder-decoder based octave convolution network . Comparing to other convolution network based methods that utilize vanilla convolution for feature extraction , the proposed method adopts octave convolution for multiple-spatial-frequency features learning , thus can better capture retinal vasculature with varying size and shape . We empirically demonstrate that the feature map of low-frequency kernels responds focus on the major vascular tree , whereas the high-frequency feature map can better captures the minor details of low contrasted thin vessels . To provide the network capability of learning how to decode multifrequency features , we extended octave convolution and proposed a novel operation named octave transposed convolution with the same multifrequency approach . We also proposed a novel encoder-decoder based fully convolution network named Octave UNet that generates high resolution vessel segmentation in single forward feeding . We test this hypothesis by evaluating on two noisy speech corpora – Aurora-4 and AMI . We extend the octave convolution concept to multiple resolution groups and multiple octaves . To evaluate the robustness of the inferred representations , we report the similarity between clean and noisy encodings using an affine projection loss as a proxy robustness measure . Specifically , we propose a novel Frequency Separation Network ( FSN ) for image super-resolution ( SR ) . In FSN , a new Octave Convolution ( OC ) is adopted , which uses four operations to perform information update and frequency communication between high frequency and low frequency features . In addition , global and hierarchical feature fusion are employeed to learn elaborate and comprehensive feature representations , in order to further benefit the quality of final image reconstruction . Similarly , the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies . In this work , we propose to factorize the mixed feature maps by their frequencies , and design a novel Octave Convolution ( OctConv ) operation to store and process feature maps that vary spatially “ slower ” at a lower spatial resolution reducing both memory and computation cost . Unlike existing multi-scale methods , OctConv is formulated as a single , generic , plug-and-play convolutional unit that can be used as a direct replacement of ( vanilla ) convolutions without any adjustments in the network architecture . Therefore , applying deep networks in real-world scenarios remains an open challenge , especially when using devices with limitations in hardware and computing power , e.g. , robots or mobile devices . To address this challenge , in this paper , we propose a lightweight and efficient octave convolutional neural network for fire recognition in visual scenes . Extensive experiments are conducted on FireSense , CairFire , FireNet , and FiSmo datasets . Our experiments on CIFAR , ImageNet , Food-101 and MPII show that our method has better focus on the region of interest and better accuracy than existing methods , at a lower computational complexity . Moreover , we provide an efficient CUDA implementation of our dynamic convolutions using a gather-scatter approach , achieving a significant improvement in inference speed on MobileNetV2 and ShuffleNetV2 . On human pose estimation , a task that is inherently spatially sparse , the processing speed is increased by 60 % with no loss in accuracy . By modeling the discretized sphere as a graph , we can accommodate non-uniformly distributed , partial , and changing samplings . Moreover , graph convolutions are computationally more efficient than spherical convolutions . As equivariance is desired to exploit rotational symmetries , we discuss how to approach rotation equivariance using the graph neural network introduced in Defferrard et al. ( 2016 ) . As a proof of concept , we conclude with an assessment of the performance of spherical convolutions in the context of molecular modelling , by considering structural environments within proteins . We show that the models are capable of learning non-trivial functions in these molecular environments , and that our spherical convolutions generally outperform standard 3D convolutions in this setting . In particular , despite the lack of any domain specific feature-engineering , we demonstrate performance comparable to state-of-the-art methods in the field , which build on decades of domain-specific knowledge . We propose a new layer in Convolutional Neural Networks ( CNNs ) to increase their robustness to several types of noise perturbations of the input images . We call this a push-pull layer and compute its response as the combination of two half-wave rectified convolutions , with kernels of opposite polarity . It is based on a biologically-motivated non-linear model of certain neurons in the visual system that exhibit a response suppression phenomenon , known as push-pull inhibition . The convolutional kernel is applied on a neighborhood obtained from a local affinity representation based on the Euclidean distance between 3D points . Intuitively , geodesic convolutions can easily separate objects that are spatially close but have disconnected surfaces , while Euclidean convolutions can represent interactions between nearby objects better , as they are oblivious to object surfaces . To realize a multi-resolution architecture , we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations . JSCN will simultaneously operate multi-layer spectral convolutions on different graphs , and jointly learn a domain-invariant user representation with a domain adaptive user mapping module . As a result , the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping . The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other . Particularly , three important contributions are presented throughout this paper . First , fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture . Second , instead of batch normalization , we propose a spectral normalization layer which improves the selectivity of filters by normalizing their spectral responses . Its discrete version in terms of the negative binomial coefficients is proved as well . The new bounds for the Dirichlet distribution and iterated convolutions are obtained as the consequences of the main result . Also some binomial , exponential , and generalized hypergeometric applications are discussed . One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset . We propose conditionally parameterized convolutions ( CondConv ) , which learn specialized convolutional kernels for each example . Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network , while maintaining efficient inference . In this work , we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions . For this , we adopt sparse tensors and propose generalized sparse convolutions that encompass all discrete convolutions . To implement the generalized sparse convolution , we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks . 2.3 Alternative tests of the summation property How might we test the implication described by Eq. 3 ? Ashby and T ownsend ( 1980 ) suggested directly comparing the tw o convolutions to test the SIstage Model . To use the relation in the way they advocate , howe ver , requires estimating density functions ( Silverman , 1986 ) d̂ij ( t ) for each of the four { RTij } sets , then using numerical con volution of d̂12(t ) with d̂21(t ) and d̂11(t ) with d̂22(t ) , and comparing the results . document : Depthwise Separable Convolutions for Neural Machine Translation Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency . subsection : Convolutional modules To perform local computation , we use modules of convolutions with ReLU non - linearities and layer normalization . A module of convolutions gets as input a tensor of shape [ sequence length , feature channels ] and returns a tensor of the same shape . Much of the original gains of the GoogLeNet network arise from a very generous use of dimension reduction . This can be viewed as a special case of factorizing convolutions in a computationally efficient manner . Consider for example the case of a convolutional layer followed by a convolutional layer . The contracting path follows the typical architecture of a convolutional network . It consists of the repeated application of two 3x3 convolutions ( unpadded convolutions ) , each followed by a rectified linear unit ( ReLU ) and a 2x2 max pooling operation with stride 2 for downsampling . At each downsampling step we double the number of feature channels . Note also that we employ Matlab ’s sparse matrix data structure functionality to represent and , which also provides a speed boost when multiplying these matrices to carry out the convolutions . If we do not use the matrix - multiplication method for convolution , and instead apply two - dimensional convolutions to each individual image , the feature generation is slowed even more . For the classifier stage , on MNIST with , the runtime is approximately 150 seconds for ( there is a small time penalty for smaller , due to the larger dimension of the input to the classifier stage ) . Hence , we propose an efficient approach to fuse multi-scale deep representations , called convolutional fusion networks ( CFN ) . Owing to using 1 \(\times \ ) 1 convolution and global average pooling , CFN can efficiently generate the side branches while adding few parameters . In addition , we present a locally-connected fusion module , which can learn adaptive weights for the side branches and form a discriminatively fused feature . For instance , the high-resolution sub-network with fewer stages is applied to local high-frequency textured information extraction , while the low-resolution one with more stages is devoted to generating global low-frequency information . Furthermore , the fusion block with channel-wise sub-network attention is proposed for adaptively fusing the feature maps from different sub-networks instead of applying concatenation and $ 1\times 1 $ convolution . A series of ablation investigations and model analyses validate the effectiveness and efficiency of our MARDN . Glow first introduced a simple type of generative flow using an invertible 1x1 convolution . However , the 1x1 convolution suffers from limited flexibility compared to the standard convolutions . In this paper , we propose a novel invertible nxn convolution approach that overcomes the limitations of the invertible 1x1 convolution . Deep learning methods are confirmed to be helpful to improve the accuracy of weather prediction . In this work , weather prediction based on the hybrid model using 1D CNN ( 1-Dimensional Convolution ) and Bi-LSTM ( Bidirectional Long Short-Term Memory ) is proposed , which could capture more useful features . Experiment results show that the model combining Bi-LSTM and 1D-CNN algorithm can achieve higher precision of prediction compared with FNN , 1D-CNN , LSTM , Bi-LSTM algorithm . Moreover , a novel architecture , named the temporal convolutional recurrent network ( TCRN ) , is proposed for online HCTR . Specifically , the TCRN first applies the 1-dimensional convolution to extract local contextual features from low-level trajectories , and then it utilizes the recurrent network to capture long-term dependencies of high-level outputs . Compared with the state-of-the-art architecture , the TCRN not only avoids the domain-specific knowledge for feature image extraction , but also attains higher training efficiency with a more compact model . The present invention relates to a kind of paper sheet defect detection methods based on local luminance invariance priori , contain following steps : 1 , treating detection paper surface using colored area array cameras and take pictures , one frame pixel format of capture is the color image of RGB component；2 , nonlinear gray operation is carried out using all pixels of the convolution mask of a local luminance invariance priori to the color image in the convolution mask mask , finally obtains pixel corresponding to convolution window；Two gray level images are respectively obtained according to 1 × 1 convolution operation and 3 × 3 convolution operations；3 , corresponding position pixel is carried out to two gray level images to be divided by , obtain a kind of grayscale image；4,3 × 3 Fourier 's convolution algorithm is carried out to grayscale image；5 , gradient morphologies operation is carried out to operation result；6 , thresholding processing is carried out , paper sheet defect testing result is obtained；The detection of ordinary light source and area array cameras to paper sheet defect can be used in the present invention , and the complexity of detection system is low , and accuracy is high , at low cost . At first , in order to predict the category and position of tender tea shoots , an image pyramid structure is used to obtain the characteristic map of tea shoots at different scales . The residual network block structure is added to the downsampling part , and the fully connected part is replaced by a $ 1\times 1 $ convolution operation at the end , ensuring accurate identification of the result and simplifying the network structure . The K-means method is used to cluster the dimension of the target box . In many real-time applications , the deployment of deep neural networks is constrained by high computational cost and efficient lightweight neural networks are widely concerned . In this paper , we propose that depthwise convolution ( DWC ) is used to expand the number of channels in a bottleneck block , which is more efficient than 1 x 1 convolution . The proposed Pointwise-Standard-Depthwise network ( PSDNet ) based on channel expansion with DWC has fewer number of parameters , less computational cost and higher accuracy than corresponding ResNet on CIFAR datasets . Self attention mechanisms have become a key building block in many state-of-the-art language understanding models . In this paper , we show that the self attention operator can be formulated in terms of 1x1 convolution operations . Following this observation , we propose several novel operators : First , we introduce a 2D version of self attention that is applicable for 2D signals such as images . Secondly , we use two convolutional layers with kernal size and respectively ( is set to the default value 7 ) to further abstract the global representation of RoI. Finally , the output of 1x1 convolution is fed into the classifier whose output is also a - d vector . In addition , context prior is the most basic and important factor for visual recognition tasks . the depth and number of parameters in the network gradually increases to model C. Several things are to be noted here . First , as described in the table , all base networks we consider use a 1 - by - 1 convolution at the top to produce 10 outputs of which we then compute an average over all positions and a softmax to produce class - probabilities ( see Section [ reference ] for the rationale behind this approach ) . We performed additional experiments with fully connected layers instead of 1 - by - 1 convolutions but found these models to consistently perform worse than their fully convolutional counterparts . We remove the top - most linear classifier and the global pooling layer , and then consider two cases . For one thing , we follow a basic large field - of - view setting in DeepLab - v2 , called ‘ 1 convolution ’ . Namely , we just add back a single linear layer as the new classifier . First , it forms the pooling stream input of the next FRRU in the network and second it is the basis for the computed residual . To this end , we first adjust the number of feature channels using a 1 × 1 convolution and then upscale the spatial dimensions using an unpooling layer . Because the features might have to be upscaled significantly ( e.g. , by a factor of 16 ) , we found that simply upscaling by repeating the entries along the spatial dimensions performed superior to bilinear interpolation . The resulting ResNet has slightly fewer parameters than the original FRRN ( 16.7 × 10 6 vs. 17.7 × 10 6 ) . This is due to the fact that RUs lack the 1 × 1 convolutions that connect the pooling to the residual Table 2 . IoU scores from the cityscapes test set .
Then we divide the service time into two independent parts and derive distributions of these two parts respectively in a recursive way . With the convolution technique we obtain the service time distribution of every queuing system . Sample numerical example is presented at the end .
The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates . Facing the challenge of the classification task , we propose a novel 3D convolution neural network ( CNN ) to reduce false positives in lung nodule detection . The novel 3D CNN includes embedded multiple branches in its structure .
In this demonstration , we will reveal a full functional wireless access for vehicular environments ( WAVE ) prototype , which achieves the complete functions defined by the IEEE 802.11p standard through integrating effective and novel base-band algorithms including fast time-synchronization , multiple-stage multiple Doppler detection , accurate channel estimate , and joint QAM demodulation and convolution decoding , all upon the Cyclone III field programmable gate array ( FPGA ) development kit from the Terasic Company . The WAVE prototype has been extensively tested under various road conditions showing satisfied performances .
Using the simulated annealing arithmetic we predigested the energy function of the neural network . On the basis of the trellis diagram of the convolution codes and the new energy function of the neural network , a new neural network decoding method of the convolution decoding is presented . The simulation result shows that the network can always find global optimum .
A quantitative SPECT ( single photon emission computed tomography ) regime based on simultaneous acquisition of emission and transmission data has been developed and validated . Transmission data are acquired using a scanning collimated line source and incorporated into a modified convolution-subtraction scatter correction method which produces transmission-dependent scatter fractions . This method tailors scatter fraction to attenuation path length , falling to zero outside the body .
Here , we propose a mass detection CAD system that is based on Faster R-CNN . First , we applied a novel convolution network in the backbone of Faster R-CNN , namely deformable convolution network ( DCN ) , which improves the detection of lesions with varying shapes and sizes . Second , the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map .
However , the detection result of images with complex background is unsatisfactory , so as images with dense and small objects . Aiming at these problems , we propose a method that combined Feature Pyramid Network(FPN ) and Deformable Convolution Network(DCN ) to improve the Faster RCNN framework , which helps to improve the detection result . The improved network combines the low-level structural information and the high-level semantic information together to enhance the feature representation .
In this paper , a method for determining grasping position and angle of sea cucumber by rotatable bounding box is proposed . Firstly , an improved full convolution image segmentation network model is proposed . Secondly , the expanded sea cucumber image is used to train the improved full convolution neural network . Thirdly , we use the trained neural network to segment the image to get the sea cucumber target . Finally , the grasping position and angle of sea cucumber are determined according to the minimum enclosing rectangle of the largest connected region of the segmented image . Experiments show that the improved full convolution image segmentation network model can effectively improve the accuracy and speed of sea cucumber target segmentation . The proposed method can determine the grasping position and angle of sea cucumber at any rotation angle in real time and accurately .
The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution . Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters .
This means that no information about motion needs to be transmitted to the decoder . The irregular sampling of the prediction , due to motion estimates with sub-pixel accuracy , is dealt with using a new method , continuous normalized convolution . Image values on an interlaced sampling grid are estimated from the irregularly sampled predictions using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the integration filter .
The challenge is caused by significant variability of eye appearance in illumination , shape , color , viewing angle and dynamics , and computation related issues . In this paper , we propose a convolution-based means of gradient method to efficiently and accurately locate the eye center in low resolution images . Priority of enhancing its computation is achieved by the use of FFT transform and fewer identified pixels of circular boundary of potential eye centres .
The principal idea of obtaining optimal conditions is locally adjoint mappings . Then , applying infimal convolution concept of convex functions , step by step , we construct the dual problems for third-order discrete , discrete-approximate and differential inclusions and prove duality results . It appears that the Euler – Lagrange-type inclusions are duality relations for both primary and dual problems and that the dual problem for discrete-approximate problem makes a bridge between the dual problems of discrete and continuous problems .
Our sDropout randomly splits the data into two subsets and keeps both rather than discards one subset . We also introduce two rotational convolution techniques , i.e. rotate-pooling convolution ( RPC ) and flip-rotate-pooling convolution ( FRPC ) to boost CNNs ' performance on the robustness for rotation transformation . These two techniques encode rotation invariance into the network without adding extra parameters .
In this example , consists of a sequence of layers : Conv - BN - ReLU - Conv - BN , where Conv and BN stands for Convolution and Batch Normalization respectively . This construction scheme is adopted in all our experiments while reproducing the results of .
The unit first concatenates the two incoming streams by using a pooling layer in order to reduce the size of the residual stream . Then the concatenated features are fed through two convolution units . Each convolution unit consists of a 3 × 3 convolution layer followed by a batch normalization layer [ reference ] and a ReLU activation function .
As illustrated in Figure [ reference ] , it takes as input the embedding of words ( often trained beforehand with unsupervised methods ) in the sentence aligned sequentially , and summarize the meaning of a sentence through layers of convolution and pooling , until reaching a fixed length vectorial representation in the final layer . As in most convolutional models , we use convolution units with a local “ receptive field ” and shared weights , but we design a large feature map to adequately model the rich structures in the composition of words . paragraph : Convolution
As explained before , this technique presents two main drawbacks : reduction of segmentation accuracy and low efficiency . An alternative approach , known as fully CNNs ( FCNNs ) , mitigates these limitations by considering the network as a single non - linear convolution , which is trained in an end - to - end fashion . An important advantage of FCNNs , compared to standard CNNs , is that they can be applied to images of arbitrary size .
However , recent attempts to benefit from temporal correlations in video super - resolution have been limited to naive or inefficient architectures . In this paper , we introduce spatio - temporal sub - pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real - time speed . Specifically , we discuss the use of early fusion , slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames .
, “ Im - ” ) , we design a coupled word embedding learned from character - level embedding and word - level embedding . We first design a character - level convolution neural network ( Char - CNN ) to obtain character - level embedding . Different from , the designed Char - CNN is a fully convolutional network without max - pooling layer to capture better semantic information in character chunk .
A middle ground of directly connecting the highest convolutional features to the input and output respectively of the generator and discriminator worked well . The first layer of the GAN , which takes a uniform noise distribution as input , could be called fully connected as it is just a matrix multiplication , but the result is reshaped into a 4 - dimensional tensor and used as the start of the convolution stack . For the discriminator , the last convolution layer is flattened and then fed into a single sigmoid output .
This restriction was later alleviated in by introducing modern practices for recurrent neural network training to the original graph neural network framework . introduced a convolution - like propagation rule on graphs and methods for graph - level classification . Their approach requires to learn node degree - specific weight matrices which does not scale to large graphs with wide node degree distributions .
The predictor outputs a shape as an N × 3 matrix M , each row containing the coordinates of one point . The encoder is a composition of convolution and ReLU layers ; in addition , a random vector r is subsumed so that it perturbs the prediction from the image I.
This leads to predictions of object classes at different positions which can then simply be averaged over the whole image . This scheme was first described by Lin_2014 and further regularizes the network as the one by one convolution has much less parameters than a fully connected layer . Overall our architecture is thus reduced to consist only of convolutional layers with rectified linear non - linearities and an averaging + softmax layer to produce predictions over the whole image .
In this paper , we proposed an effective and efficient method for accurate vessel segmentation in color fundus images using encoder-decoder based octave convolution network . Comparing to other convolution network based methods that utilize vanilla convolution for feature extraction , the proposed method adopts octave convolution for multiple-spatial-frequency features learning , thus can better capture retinal vasculature with varying size and shape . We empirically demonstrate that the feature map of low-frequency kernels responds focus on the major vascular tree , whereas the high-frequency feature map can better captures the minor details of low contrasted thin vessels . To provide the network capability of learning how to decode multifrequency features , we extended octave convolution and proposed a novel operation named octave transposed convolution with the same multifrequency approach . We also proposed a novel encoder-decoder based fully convolution network named Octave UNet that generates high resolution vessel segmentation in single forward feeding .
We test this hypothesis by evaluating on two noisy speech corpora – Aurora-4 and AMI . We extend the octave convolution concept to multiple resolution groups and multiple octaves . To evaluate the robustness of the inferred representations , we report the similarity between clean and noisy encodings using an affine projection loss as a proxy robustness measure .
Specifically , we propose a novel Frequency Separation Network ( FSN ) for image super-resolution ( SR ) . In FSN , a new Octave Convolution ( OC ) is adopted , which uses four operations to perform information update and frequency communication between high frequency and low frequency features . In addition , global and hierarchical feature fusion are employeed to learn elaborate and comprehensive feature representations , in order to further benefit the quality of final image reconstruction .
Similarly , the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies . In this work , we propose to factorize the mixed feature maps by their frequencies , and design a novel Octave Convolution ( OctConv ) operation to store and process feature maps that vary spatially “ slower ” at a lower spatial resolution reducing both memory and computation cost . Unlike existing multi-scale methods , OctConv is formulated as a single , generic , plug-and-play convolutional unit that can be used as a direct replacement of ( vanilla ) convolutions without any adjustments in the network architecture .
Therefore , applying deep networks in real-world scenarios remains an open challenge , especially when using devices with limitations in hardware and computing power , e.g. , robots or mobile devices . To address this challenge , in this paper , we propose a lightweight and efficient octave convolutional neural network for fire recognition in visual scenes . Extensive experiments are conducted on FireSense , CairFire , FireNet , and FiSmo datasets .
Our experiments on CIFAR , ImageNet , Food-101 and MPII show that our method has better focus on the region of interest and better accuracy than existing methods , at a lower computational complexity . Moreover , we provide an efficient CUDA implementation of our dynamic convolutions using a gather-scatter approach , achieving a significant improvement in inference speed on MobileNetV2 and ShuffleNetV2 . On human pose estimation , a task that is inherently spatially sparse , the processing speed is increased by 60 % with no loss in accuracy .
By modeling the discretized sphere as a graph , we can accommodate non-uniformly distributed , partial , and changing samplings . Moreover , graph convolutions are computationally more efficient than spherical convolutions . As equivariance is desired to exploit rotational symmetries , we discuss how to approach rotation equivariance using the graph neural network introduced in Defferrard et al. ( 2016 ) .
As a proof of concept , we conclude with an assessment of the performance of spherical convolutions in the context of molecular modelling , by considering structural environments within proteins . We show that the models are capable of learning non-trivial functions in these molecular environments , and that our spherical convolutions generally outperform standard 3D convolutions in this setting . In particular , despite the lack of any domain specific feature-engineering , we demonstrate performance comparable to state-of-the-art methods in the field , which build on decades of domain-specific knowledge .
We propose a new layer in Convolutional Neural Networks ( CNNs ) to increase their robustness to several types of noise perturbations of the input images . We call this a push-pull layer and compute its response as the combination of two half-wave rectified convolutions , with kernels of opposite polarity . It is based on a biologically-motivated non-linear model of certain neurons in the visual system that exhibit a response suppression phenomenon , known as push-pull inhibition .
The convolutional kernel is applied on a neighborhood obtained from a local affinity representation based on the Euclidean distance between 3D points . Intuitively , geodesic convolutions can easily separate objects that are spatially close but have disconnected surfaces , while Euclidean convolutions can represent interactions between nearby objects better , as they are oblivious to object surfaces . To realize a multi-resolution architecture , we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations .
JSCN will simultaneously operate multi-layer spectral convolutions on different graphs , and jointly learn a domain-invariant user representation with a domain adaptive user mapping module . As a result , the high-order comprehensive connectivity information can be extracted by the spectral convolutions and the information can be transferred across domains with the domain-invariant user mapping . The domain adaptive user mapping module can help the incompatible domains to transfer the knowledge across each other .
Particularly , three important contributions are presented throughout this paper . First , fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture . Second , instead of batch normalization , we propose a spectral normalization layer which improves the selectivity of filters by normalizing their spectral responses .
Its discrete version in terms of the negative binomial coefficients is proved as well . The new bounds for the Dirichlet distribution and iterated convolutions are obtained as the consequences of the main result . Also some binomial , exponential , and generalized hypergeometric applications are discussed .
One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset . We propose conditionally parameterized convolutions ( CondConv ) , which learn specialized convolutional kernels for each example . Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network , while maintaining efficient inference .
In this work , we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions . For this , we adopt sparse tensors and propose generalized sparse convolutions that encompass all discrete convolutions . To implement the generalized sparse convolution , we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks .
2.3 Alternative tests of the summation property How might we test the implication described by Eq. 3 ? Ashby and T ownsend ( 1980 ) suggested directly comparing the tw o convolutions to test the SIstage Model . To use the relation in the way they advocate , howe ver , requires estimating density functions ( Silverman , 1986 ) d̂ij ( t ) for each of the four { RTij } sets , then using numerical con volution of d̂12(t ) with d̂21(t ) and d̂11(t ) with d̂22(t ) , and comparing the results .
document : Depthwise Separable Convolutions for Neural Machine Translation Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency .
subsection : Convolutional modules To perform local computation , we use modules of convolutions with ReLU non - linearities and layer normalization . A module of convolutions gets as input a tensor of shape [ sequence length , feature channels ] and returns a tensor of the same shape .
Much of the original gains of the GoogLeNet network arise from a very generous use of dimension reduction . This can be viewed as a special case of factorizing convolutions in a computationally efficient manner . Consider for example the case of a convolutional layer followed by a convolutional layer .
The contracting path follows the typical architecture of a convolutional network . It consists of the repeated application of two 3x3 convolutions ( unpadded convolutions ) , each followed by a rectified linear unit ( ReLU ) and a 2x2 max pooling operation with stride 2 for downsampling . At each downsampling step we double the number of feature channels .
Note also that we employ Matlab ’s sparse matrix data structure functionality to represent and , which also provides a speed boost when multiplying these matrices to carry out the convolutions . If we do not use the matrix - multiplication method for convolution , and instead apply two - dimensional convolutions to each individual image , the feature generation is slowed even more . For the classifier stage , on MNIST with , the runtime is approximately 150 seconds for ( there is a small time penalty for smaller , due to the larger dimension of the input to the classifier stage ) .
Hence , we propose an efficient approach to fuse multi-scale deep representations , called convolutional fusion networks ( CFN ) . Owing to using 1 \(\times \ ) 1 convolution and global average pooling , CFN can efficiently generate the side branches while adding few parameters . In addition , we present a locally-connected fusion module , which can learn adaptive weights for the side branches and form a discriminatively fused feature .
For instance , the high-resolution sub-network with fewer stages is applied to local high-frequency textured information extraction , while the low-resolution one with more stages is devoted to generating global low-frequency information . Furthermore , the fusion block with channel-wise sub-network attention is proposed for adaptively fusing the feature maps from different sub-networks instead of applying concatenation and $ 1\times 1 $ convolution . A series of ablation investigations and model analyses validate the effectiveness and efficiency of our MARDN .
Glow first introduced a simple type of generative flow using an invertible 1x1 convolution . However , the 1x1 convolution suffers from limited flexibility compared to the standard convolutions . In this paper , we propose a novel invertible nxn convolution approach that overcomes the limitations of the invertible 1x1 convolution .
Deep learning methods are confirmed to be helpful to improve the accuracy of weather prediction . In this work , weather prediction based on the hybrid model using 1D CNN ( 1-Dimensional Convolution ) and Bi-LSTM ( Bidirectional Long Short-Term Memory ) is proposed , which could capture more useful features . Experiment results show that the model combining Bi-LSTM and 1D-CNN algorithm can achieve higher precision of prediction compared with FNN , 1D-CNN , LSTM , Bi-LSTM algorithm .
Moreover , a novel architecture , named the temporal convolutional recurrent network ( TCRN ) , is proposed for online HCTR . Specifically , the TCRN first applies the 1-dimensional convolution to extract local contextual features from low-level trajectories , and then it utilizes the recurrent network to capture long-term dependencies of high-level outputs . Compared with the state-of-the-art architecture , the TCRN not only avoids the domain-specific knowledge for feature image extraction , but also attains higher training efficiency with a more compact model .
The present invention relates to a kind of paper sheet defect detection methods based on local luminance invariance priori , contain following steps : 1 , treating detection paper surface using colored area array cameras and take pictures , one frame pixel format of capture is the color image of RGB component；2 , nonlinear gray operation is carried out using all pixels of the convolution mask of a local luminance invariance priori to the color image in the convolution mask mask , finally obtains pixel corresponding to convolution window；Two gray level images are respectively obtained according to 1 × 1 convolution operation and 3 × 3 convolution operations；3 , corresponding position pixel is carried out to two gray level images to be divided by , obtain a kind of grayscale image；4,3 × 3 Fourier 's convolution algorithm is carried out to grayscale image；5 , gradient morphologies operation is carried out to operation result；6 , thresholding processing is carried out , paper sheet defect testing result is obtained；The detection of ordinary light source and area array cameras to paper sheet defect can be used in the present invention , and the complexity of detection system is low , and accuracy is high , at low cost .
At first , in order to predict the category and position of tender tea shoots , an image pyramid structure is used to obtain the characteristic map of tea shoots at different scales . The residual network block structure is added to the downsampling part , and the fully connected part is replaced by a $ 1\times 1 $ convolution operation at the end , ensuring accurate identification of the result and simplifying the network structure . The K-means method is used to cluster the dimension of the target box .
In many real-time applications , the deployment of deep neural networks is constrained by high computational cost and efficient lightweight neural networks are widely concerned . In this paper , we propose that depthwise convolution ( DWC ) is used to expand the number of channels in a bottleneck block , which is more efficient than 1 x 1 convolution . The proposed Pointwise-Standard-Depthwise network ( PSDNet ) based on channel expansion with DWC has fewer number of parameters , less computational cost and higher accuracy than corresponding ResNet on CIFAR datasets .
Self attention mechanisms have become a key building block in many state-of-the-art language understanding models . In this paper , we show that the self attention operator can be formulated in terms of 1x1 convolution operations . Following this observation , we propose several novel operators : First , we introduce a 2D version of self attention that is applicable for 2D signals such as images .
Secondly , we use two convolutional layers with kernal size and respectively ( is set to the default value 7 ) to further abstract the global representation of RoI. Finally , the output of 1x1 convolution is fed into the classifier whose output is also a - d vector . In addition , context prior is the most basic and important factor for visual recognition tasks .
the depth and number of parameters in the network gradually increases to model C. Several things are to be noted here . First , as described in the table , all base networks we consider use a 1 - by - 1 convolution at the top to produce 10 outputs of which we then compute an average over all positions and a softmax to produce class - probabilities ( see Section [ reference ] for the rationale behind this approach ) . We performed additional experiments with fully connected layers instead of 1 - by - 1 convolutions but found these models to consistently perform worse than their fully convolutional counterparts .
We remove the top - most linear classifier and the global pooling layer , and then consider two cases . For one thing , we follow a basic large field - of - view setting in DeepLab - v2 , called ‘ 1 convolution ’ . Namely , we just add back a single linear layer as the new classifier .
First , it forms the pooling stream input of the next FRRU in the network and second it is the basis for the computed residual . To this end , we first adjust the number of feature channels using a 1 × 1 convolution and then upscale the spatial dimensions using an unpooling layer . Because the features might have to be upscaled significantly ( e.g. , by a factor of 16 ) , we found that simply upscaling by repeating the entries along the spatial dimensions performed superior to bilinear interpolation .
The resulting ResNet has slightly fewer parameters than the original FRRN ( 16.7 × 10 6 vs. 17.7 × 10 6 ) . This is due to the fact that RUs lack the 1 × 1 convolutions that connect the pooling to the residual Table 2 . IoU scores from the cityscapes test set .
Elevator service time distribution for the product allocation problem in a multi-level distribution center An Embedded Multi-branch 3D Convolution Neural Network for False Positive Reduction in Lung Nodule Detection A full functional wireless access for vehicular environments (WAVE) prototype upon the IEEE 802.11p standard for vehicular communications and networks The Use of Neural Network's Energy Function in theConvolution Decoding Method A quantitative SPECT regime Automated mammographic mass detection using deformable convolution and multiscale features Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN Method for Determining Grasping Position and Angle of Sea Cucumber by Rotatable Bounding Box Continuous normalized convolution Quality and motion estimation for image sequence coding Convolution-based means of gradient for fast eye center localization Infimal Convolution and Duality in Problems with Third-Order Discrete and Differential Inclusions Flip-Rotate-Pooling Convolution and Split Dropout on Convolution Neural Networks for Image Classification Deep Residual Networks with Exponential Linear Unit Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes Convolutional Neural Network Architectures for Matching Natural Language Sentences Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Semi-Supervised Classification with Graph Convolutional Networks A Point Set Generation Network for 3D Object Reconstruction from a Single Image Striving for Simplicity: The All Convolutional Net Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network Multi-Scale Octave Convolutions for Robust Speech Recognition Frequency Separation Network for Image Super-Resolution Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution Lightweight and efficient octave convolutional neural network for fire recognition Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference DeepSphere: towards an equivariant graph-based spherical CNN Spherical convolutions and their application in molecular modelling A Push-Pull Layer Improves Robustness of Convolutional Neural Networks DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation Deep Spectral Convolution Network for Hyperspectral Unmixing An inequality for multiple convolutions with respect to Dirichlet probability measure CondConv: Conditionally Parameterized Convolutions for Efficient Inference 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks Some Constraints on Reaction-Time Distributions for Sequential Processes Depthwise Separable Convolutions for Neural Machine Translation Rethinking the Inception Architecture for Computer Vision U-Net: Convolutional Networks for Biomedical Image Segmentation Enhanced image classification with a fast-learning shallow convolutional neural network On the Exploration of Convolutional Fusion Networks for Visual Recognition Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution Generative Flow via Invertible nxn Convolution Multi-Stations’ Weather Prediction Based on Hybrid Model Using 1D CNN and Bi-LSTM In-air handwritten Chinese text recognition with temporal convolutional recurrent network Paper sheet defect detection method based on local luminance invariance priori Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block Multiscale Self Attentive Convolutions for Vision and Language Modeling CoupleNet: Coupling Global Structure with Local Parts for Object Detection Striving for Simplicity: The All Convolutional Net Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
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CCNC ICME (1) ICMLC VisionNet'16 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS Comput. Medical Imaging Graph. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACL ICLR ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ICASSP LA-CCI NIPS ICIP NeurIPS ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) MICCAI 2015 International Joint Conference on Neural Networks (IJCNN) MMM (1) 2017 IEEE International Conference on Computer Vision (ICCV) ICLR Pattern Recognit. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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