To address this issue , we present Dynamic Convolution , a new design that increases model complexity without increasing the network depth or width . Instead of using a single convolution kernel per layer , dynamic convolution aggregates multiple parallel convolution kernels dynamically based upon their attentions , which are input dependent . Assembling multiple kernels is not only computationally efficient due to the small kernel size , but also has more representation power since these kernels are aggregated in a non-linear way via attention . For the MAB segmentation , we convert the segmentation problem to a pixel‐by‐pixel classification problem . A dynamic convolutional neural network ( Dynamic CNN ) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine‐tuned dynamically in each test task . The LIB is segmented by applying a region‐of‐interest of carotid images to a U‐Net model , which allows the network to be trained end‐to‐end for pixel‐wise classification . This study aims to classify the sentiment is positive , negative and neutral from opinions Indonesian language movie and look for the accuracy , precission , recall and f-meausre of the method used is Dynamic Convolutional Neural Network . The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method , the value of accuracy of 80,99 % , the value of precission 81,00 % , recall 81,00 % , f-measure 79,00 % while the value of the resulting accuracy Naive Bayes amounted to 76,21 % , precission 78,00 % , recall 76,00 % , f-measure 75,00 % . The conventional convolutional layer uses filters that are learned during training and are held constant during testing . In contrast , the dynamic convolutional layer uses filters that will vary from input to input during testing . This is achieved by learning a function that maps the input to the filters . We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines . The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better . For the purpose of detecting brain function activation , according to the principle of GLM , a new convolution model is presented by a new dynamic function convolving with design-matrix , which combining with t-test can be used to detect brain active signal . The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among v1and v2 areas of visual cortex , and also verified the validity of this technique . The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better . For the purpose of detecting brain function activation , according to the principle of GLM , a new convolution model is presented by a new dynamic function convolving with design-matrix , which combining with t-test can be used to detect brain active signal . The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among vland v2 areas of visual cortex , and also verified the validity of this technique . A technique is shown , to treat the effects of water scattering . This is done by combining the so-called dynamic derivative convolution method and a maximum-likelihood approach . Several low energy peaks can be reconstructed . The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties . Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers . This chapter considers the model-based design , in which the optimal design problem requires two key features to be specified : ( i ) a statistical or mathematical model for the process under consideration ; and , ( ii ) a criterion with respect to which the design is required to be optimized . After reviewing spatial and spatio-temporal adaptive designs it considers the performance of adaptive design-finding algorithms with respect to these for two different models for stochastic process S : the stationary Gaussian model ; and a dynamic process convolution model . The chapter uses the second of these models to consider adaptive designs for the Upper Austria rainfall data . This is column “ All - CNN - C ” in the table . A model in which a dense convolution is placed before each max - pooling layer ( the additional convolutions have the same kernel size as the respective pooling layer ) . This is model “ ConvPool - CNN - C ” in the table . We can also extend the QRNN architecture using techniques introduced for convolutional networks . For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of . The functions and are nonlinear transformations applied termwise to matrix inputs to produce matrix outputs of the same size . The symbol * represents two - dimensional convolution . InputInput OutputOutput Convolutional feature detection . Let and let be a discrete filter of size . The discrete convolution operator can be defined as We now generalize this operator . Let be a dilation factor and let be defined as We will refer to as a dilated convolution or an - dilated convolution . The familiar discrete convolution is simply the - dilated convolution . The dilated convolution operator has been referred to in the past as “ convolution with a dilated filter ” . There is very little work on convolutional modeling of language . In addition to , there is a very recent model on sentence representation with dynamic convolutional neural network . This work relies heavily on a carefully designed pooling strategy to handle the variable length of sentence with a relatively small feature map , tailored for classification problems with modest sizes . We demonstrate the efficacy of our method , named CondConv , by scaling up the MobileNetV1 , MobileNetV2 , and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference . On the ImageNet classification dataset , CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x ) model from 63.8 % to 71.6 % while only increasing inference cost by 27 % . On COCO object detection , CondConv improves the minival mAP of a MobileNetV1(1.0x ) SSD model from 20.3 to 22.4 with just a 4 % increase in inference cost . We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks . On ImageNet classification , our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3 % accuracy with only 413 M multiply-adds . Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at : https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv . In this paper , we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place ( top 1 % ) in the mentioned competition . Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as our base architecture , we implemented Spatial-Channel Squeeze & Excitation , Lovasz loss , CoordConv and Hypercolumn methods . The source code for our solution is made publicly available at this https URL . We demonstrate and carefully analyze the failure first on a toy problem , at which point a simple fix becomes obvious . We call this solution CoordConv , which works by giving convolution access to its own input coordinates through the use of extra coordinate channels . Without sacrificing the computational and parametric efficiency of ordinary convolution , CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence , as required by the end task . CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10 - -100 times fewer parameters than convolution . The proposed framework firstly aligns an input CT image into the standard space . Then , the aligned image is processed by a midline detection network ( MD-Net ) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map . Finally , we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation . We propose a deep feature fusion method utilising features at multiple scales for learning self-supervised depth from scratch . Our fusion network selects features from both upper and lower levels at every level in the encoder network , thereby creating multiple feature pyramid sub-networks that are fed to the decoder after applying the CoordConv solution . We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth using a pixel shuffling framework that super-resolves lower level residual depth . Our proof is based on an efficient method of increasing regularity : the solution is rewritten as the sum of two processes , one solves a linear parabolic SPDE with the same noise term as the original model problem whereas the other solves a linear parabolic PDE with random coefficients . This way , the required regularity can be achieved by repeatedly making use of known techniques for stochastic convolutions and deterministic PDEs . This paper presents an efficient data-parallel algorithm for general convolutions , and compares its performance on the CM-5 to FFT frequency filtering . Sequential FFT filters are faster than sequential convolutions for windows beyond a very small size , typically 6/spl times/6 pixels . Our folded convolution algorithm shifts the convolution/FFT performance crossover to much larger filter sizes . We present and compare two design techniques for a large class of 2-D FIR filters . They can be implemented by means of sequential convolutions with small size kernels . Design methods are based upon the minimization of either the mean square or the maximum error between the synthesized filter and a design prototype in the Fourier domain . We propose ACS ( axial-coronal-sagittal ) convolutions to perform natively 3D representation learning , while utilizing the pretrained weights from 2D counterparts . In ACS convolutions , 2D convolution kernels are split by channel into three parts , and convoluted separately on the three views ( axial , coronal and sagittal ) of 3D representations . Theoretically , ANY 2D CNN ( ResNet , DenseNet , or DeepLab ) is able to be converted into a 3D ACS CNN , with pretrained weights of same parameter sizes . We propose ACS ( axial-coronal-sagittal ) convolutions to perform natively 3D representation learning , while utilizing the pretrained weights on 2D datasets . In ACS convolutions , 2D convolution kernels are split by channel into three parts , and convoluted separately on the three views ( axial , coronal and sagittal ) of 3D representations . Theoretically , ANY 2D CNN ( ResNet , DenseNet , or DeepLab ) is able to be converted into a 3D ACS CNN , with pretrained weight of a same parameter size . Next , the idea of features ’ vectors inspired by CapsNet is used in the following layer of the proposed neural network . Multi-level convolutions are used to extract feature vectors of different ROIs for facial expression , and then the feature vectors are reconstructed by a decoder to reconstruct the image . Comprehensive comparative experiments and cross-database experiments are conducted to verify the validity and robustness of our proposed model . The GPU architecture seems to be a suitable architecture to accelerate these convolutions , but reaching high application performance requires substantial development time and non-portable optimizations . In this work , we present the techniques , performance results and considerations to accelerate small 2D convolutions using CUDA , and compare performance to a multi-threaded CPU implementation . To improve programmability and performance of applications that make heavy use of small convolutions , we argue that two improvements to software and hardware are needed : FFT libraries must be extended with a single convolution function and communication bandwidth between CPU and GPU needs to be drastically improved . This paper discusses a Distributed Compressed Video Sensing ( DCVS ) framework using Convolutional Sparse Coding ( CSC ) . CSC is a technique to represent a signal as convolutions of filters and corresponding coefficients . Conventional block based DCVS methods divide a given video sequence into key and non-key frames . The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network , which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data . Within the network , the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views . A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization . Deep Convolutional Neural Networks ( CNNs ) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) since 2012 , yet there is relatively no clear understanding of the reasons behind their outstanding performance , or how they might be improved , In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics : speed , memory consumption , and network parameters utilization . Key findings are : ( 1 ) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers ; ( 2 ) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types ; ( 3 ) 1 × 1 convolutions are an efficient way to reduce dimensionality and pooling features ; ( 4 ) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph , we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs . In this paper , we propose a method of improving temporal Convolutional Neural Networks ( CNN ) by determining the optimal alignment of weights and inputs using dynamic programming . Conventional CNN convolutions linearly match the shared weights to a window of the input . However , it is possible that there exists a more optimal alignment of weights . In this paper , we integrate the fast Fourier transform ( FFT ) method into the McLaughlin ’s framework , and present an improved FFT-based Montgomery modular multiplication ( MMM ) algorithm achieving high area-time efficiency . Compared to the previous FFT-based designs , we inhibit the zero-padding operation by computing the modular multiplication steps directly using cyclic and nega-cyclic convolutions . Thus , we reduce the convolution length by half . Note the original work only defined the model for . Molecular Graph Convolutions , ( ) This work deviates slightly from other MPNNs in that it introduces edge representations which are updated during the message passing phase . These input representations include the Coulomb Matrix ( CM , ) , Bag of Bonds ( BoB , ) , Bonds Angles , Machine Learning ( BAML , ) , Extended Connectivity Fingerprints ( ECPF4 , ) , and “ Projected Histograms ” ( HDAD , ) representations . In addition to these hand engineered features we include two existing baseline MPNNs , the Molecular Graph Convolutions model ( GC ) from , and the original GG - NN model trained with distance bins . Overall , our new MPNN achieves chemical accuracy on 11 out of 13 targets and state of the art on all 13 targets . ( 2016 ) have shown that text classification accuracy increases when the proposed model becomes deeper . VDCNN uses only small kernel convolutions and pooling operations . The proposed architecture relies on the VGG and ResNet philosophy : The number of feature maps and the temporal resolution is modeled so that their product is constant . Each kernel thus acts as the ultimate detector of tissue from one of the segmentation labels . We use the softmax non - linearity which normalizes the result of the kernel convolutions into a multinominal distribution over the labels . Specifically , let be the vector of values at a given spatial position , it computes where is a normalization constant . One can see similar effects when using linear activations for the dimension reduction components . subsection : Spatial Factorization into Asymmetric Convolutions The above results suggest that convolutions with filters larger a might not be generally useful as they can always be reduced into a sequence of convolutional layers . Still we can ask the question whether one should factorize them into smaller , for example convolutions . However , it turns out that one can do even better than by using asymmetric convolutions , e.g. . For example using a convolution followed by a convolution is equivalent to sliding a two layer network with the same receptive field as in a convolution ( see figure [ reference ] ) . " I " is input image , " conv " backbone convolutions , " pool " region - wise feature extraction , " H " network head , " B " bounding box , and " C " classification . " B0 " is proposals in all architectures . section : Experiments We design our experiments with the goal to answer two key questions : What is the performance impact of replacing convolutions in a ByteNet - like model with depthwise separable convolutions ? What is the performance trade - off of reducing dilation while correspondingly increasing convolution window size ? The input - to - state convolution in this layer uses a mask of type B , whereas the state - to - state convolution is not masked . The PixelCNN uses convolutions of size with a mask of type B. The top feature map is then passed through a couple of layers consisting of a Rectified Linear Unit ( ReLU ) and a convolution . However , none of these approaches has been demonstrated improvements over state of the art results on large benchmark datasets . Gated convolutions have been previously explored for machine translation by [ reference ] but their evaluation was restricted to a small dataset and the model was used in tandem with a traditional count - based model . Architec - For this reason , Bo et al . use the Nyström method for their hierarchical kernel descriptors . In this section , we show that when the coordinate sets are two - dimensional regular grids , a natural approximation for the multilayer convolutional kernel consists of a sequence of spatial convolutions with learned filters , pointwise non - linearities , and pooling operations , as illustrated in Figure [ reference ] . More precisely , our scheme approximates the kernel map of defined in ( [ reference ] ) at layer by finite - dimensional spatial maps , where is a set of coordinates related to , and is a positive integer controlling the quality of the approximation . we make use of small convolutional layers with which can greatly reduce the number of parameters in a network and thus serve as a form of regularization . Additionally , to unify the architecture further , we make use of the fact that if the image area covered by units in the topmost convolutional layer covers a portion of the image large enough to recognize its content ( i.e. the object we want to recognize ) then fully connected layers can also be replaced by simple 1 - by - 1 convolutions . This leads to predictions of object classes at different positions which can then simply be averaged over the whole image . Concatenated features are smoothed by a convolution with ReLU . Final heatmap which has layers obtained via convolutions without activation . The final output is multiplied with a binary mask of which has in the area of the persons without annotation . In this paper we study a fundamental question : How much memory is actually needed to train a neural network ? To answer this question , we profile the overall memory usage of training on two representative deep learning benchmarks -- the WideResNet model for image classification and the DynamicConv Transformer model for machine translation -- and comprehensively evaluate four standard techniques for reducing the training memory requirements : ( 1 ) imposing sparsity on the model , ( 2 ) using low precision , ( 3 ) microbatching , and ( 4 ) gradient checkpointing . We explore how each of these techniques in isolation affects both the peak memory usage of training and the quality of the end model , and explore the memory , accuracy , and computation tradeoffs incurred when combining these techniques . Using appropriate combinations of these techniques , we show that it is possible to the reduce the memory required to train a WideResNet-28 - 2 on CIFAR-10 by up to 60.7x with a 0.4 % loss in accuracy , and reduce the memory required to train a DynamicConv model on IWSLT'14 German to English translation by up to 8.7x with a BLEU score drop of 0.15 .
To address this issue , we present Dynamic Convolution , a new design that increases model complexity without increasing the network depth or width . Instead of using a single convolution kernel per layer , dynamic convolution aggregates multiple parallel convolution kernels dynamically based upon their attentions , which are input dependent . Assembling multiple kernels is not only computationally efficient due to the small kernel size , but also has more representation power since these kernels are aggregated in a non-linear way via attention .
For the MAB segmentation , we convert the segmentation problem to a pixel‐by‐pixel classification problem . A dynamic convolutional neural network ( Dynamic CNN ) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine‐tuned dynamically in each test task . The LIB is segmented by applying a region‐of‐interest of carotid images to a U‐Net model , which allows the network to be trained end‐to‐end for pixel‐wise classification .
This study aims to classify the sentiment is positive , negative and neutral from opinions Indonesian language movie and look for the accuracy , precission , recall and f-meausre of the method used is Dynamic Convolutional Neural Network . The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method , the value of accuracy of 80,99 % , the value of precission 81,00 % , recall 81,00 % , f-measure 79,00 % while the value of the resulting accuracy Naive Bayes amounted to 76,21 % , precission 78,00 % , recall 76,00 % , f-measure 75,00 % .
The conventional convolutional layer uses filters that are learned during training and are held constant during testing . In contrast , the dynamic convolutional layer uses filters that will vary from input to input during testing . This is achieved by learning a function that maps the input to the filters . We apply the dynamic convolutional layer to the application of short range weather prediction and show performance improvements compared to other baselines .
The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better . For the purpose of detecting brain function activation , according to the principle of GLM , a new convolution model is presented by a new dynamic function convolving with design-matrix , which combining with t-test can be used to detect brain active signal . The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among v1and v2 areas of visual cortex , and also verified the validity of this technique .
The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better . For the purpose of detecting brain function activation , according to the principle of GLM , a new convolution model is presented by a new dynamic function convolving with design-matrix , which combining with t-test can be used to detect brain active signal . The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among vland v2 areas of visual cortex , and also verified the validity of this technique .
A technique is shown , to treat the effects of water scattering . This is done by combining the so-called dynamic derivative convolution method and a maximum-likelihood approach . Several low energy peaks can be reconstructed .
The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties . Our contribution is an extension of the Dynamic Edge Convolution model that exploits the sequential relations of points in a fiber and discriminates with high accuracy plausible/non-plausible fibers .
This chapter considers the model-based design , in which the optimal design problem requires two key features to be specified : ( i ) a statistical or mathematical model for the process under consideration ; and , ( ii ) a criterion with respect to which the design is required to be optimized . After reviewing spatial and spatio-temporal adaptive designs it considers the performance of adaptive design-finding algorithms with respect to these for two different models for stochastic process S : the stationary Gaussian model ; and a dynamic process convolution model . The chapter uses the second of these models to consider adaptive designs for the Upper Austria rainfall data .
This is column “ All - CNN - C ” in the table . A model in which a dense convolution is placed before each max - pooling layer ( the additional convolutions have the same kernel size as the respective pooling layer ) . This is model “ ConvPool - CNN - C ” in the table .
We can also extend the QRNN architecture using techniques introduced for convolutional networks . For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of .
The functions and are nonlinear transformations applied termwise to matrix inputs to produce matrix outputs of the same size . The symbol * represents two - dimensional convolution . InputInput OutputOutput Convolutional feature detection .
Let and let be a discrete filter of size . The discrete convolution operator can be defined as We now generalize this operator . Let be a dilation factor and let be defined as We will refer to as a dilated convolution or an - dilated convolution . The familiar discrete convolution is simply the - dilated convolution . The dilated convolution operator has been referred to in the past as “ convolution with a dilated filter ” .
There is very little work on convolutional modeling of language . In addition to , there is a very recent model on sentence representation with dynamic convolutional neural network . This work relies heavily on a carefully designed pooling strategy to handle the variable length of sentence with a relatively small feature map , tailored for classification problems with modest sizes .
We demonstrate the efficacy of our method , named CondConv , by scaling up the MobileNetV1 , MobileNetV2 , and ResNet-50 model architectures to achieve higher accuracy while retaining efficient inference . On the ImageNet classification dataset , CondConv improves the top-1 validation accuracy of the MobileNetV1(0.5x ) model from 63.8 % to 71.6 % while only increasing inference cost by 27 % . On COCO object detection , CondConv improves the minival mAP of a MobileNetV1(1.0x ) SSD model from 20.3 to 22.4 with just a 4 % increase in inference cost .
We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks . On ImageNet classification , our CondConv approach applied to EfficientNet-B0 achieves state-ofthe-art performance of 78.3 % accuracy with only 413 M multiply-adds . Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at : https://github.com/tensorflow/tpu/tree/master/ models/official/efficientnet/condconv .
In this paper , we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place ( top 1 % ) in the mentioned competition . Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as our base architecture , we implemented Spatial-Channel Squeeze & Excitation , Lovasz loss , CoordConv and Hypercolumn methods . The source code for our solution is made publicly available at this https URL .
We demonstrate and carefully analyze the failure first on a toy problem , at which point a simple fix becomes obvious . We call this solution CoordConv , which works by giving convolution access to its own input coordinates through the use of extra coordinate channels . Without sacrificing the computational and parametric efficiency of ordinary convolution , CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence , as required by the end task . CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10 - -100 times fewer parameters than convolution .
The proposed framework firstly aligns an input CT image into the standard space . Then , the aligned image is processed by a midline detection network ( MD-Net ) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map . Finally , we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation .
We propose a deep feature fusion method utilising features at multiple scales for learning self-supervised depth from scratch . Our fusion network selects features from both upper and lower levels at every level in the encoder network , thereby creating multiple feature pyramid sub-networks that are fed to the decoder after applying the CoordConv solution . We also propose a refinement module learning higher scale residual depth from a combination of higher level deep features and lower level residual depth using a pixel shuffling framework that super-resolves lower level residual depth .
Our proof is based on an efficient method of increasing regularity : the solution is rewritten as the sum of two processes , one solves a linear parabolic SPDE with the same noise term as the original model problem whereas the other solves a linear parabolic PDE with random coefficients . This way , the required regularity can be achieved by repeatedly making use of known techniques for stochastic convolutions and deterministic PDEs .
This paper presents an efficient data-parallel algorithm for general convolutions , and compares its performance on the CM-5 to FFT frequency filtering . Sequential FFT filters are faster than sequential convolutions for windows beyond a very small size , typically 6/spl times/6 pixels . Our folded convolution algorithm shifts the convolution/FFT performance crossover to much larger filter sizes .
We present and compare two design techniques for a large class of 2-D FIR filters . They can be implemented by means of sequential convolutions with small size kernels . Design methods are based upon the minimization of either the mean square or the maximum error between the synthesized filter and a design prototype in the Fourier domain .
We propose ACS ( axial-coronal-sagittal ) convolutions to perform natively 3D representation learning , while utilizing the pretrained weights from 2D counterparts . In ACS convolutions , 2D convolution kernels are split by channel into three parts , and convoluted separately on the three views ( axial , coronal and sagittal ) of 3D representations . Theoretically , ANY 2D CNN ( ResNet , DenseNet , or DeepLab ) is able to be converted into a 3D ACS CNN , with pretrained weights of same parameter sizes .
We propose ACS ( axial-coronal-sagittal ) convolutions to perform natively 3D representation learning , while utilizing the pretrained weights on 2D datasets . In ACS convolutions , 2D convolution kernels are split by channel into three parts , and convoluted separately on the three views ( axial , coronal and sagittal ) of 3D representations . Theoretically , ANY 2D CNN ( ResNet , DenseNet , or DeepLab ) is able to be converted into a 3D ACS CNN , with pretrained weight of a same parameter size .
Next , the idea of features ’ vectors inspired by CapsNet is used in the following layer of the proposed neural network . Multi-level convolutions are used to extract feature vectors of different ROIs for facial expression , and then the feature vectors are reconstructed by a decoder to reconstruct the image . Comprehensive comparative experiments and cross-database experiments are conducted to verify the validity and robustness of our proposed model .
The GPU architecture seems to be a suitable architecture to accelerate these convolutions , but reaching high application performance requires substantial development time and non-portable optimizations . In this work , we present the techniques , performance results and considerations to accelerate small 2D convolutions using CUDA , and compare performance to a multi-threaded CPU implementation . To improve programmability and performance of applications that make heavy use of small convolutions , we argue that two improvements to software and hardware are needed : FFT libraries must be extended with a single convolution function and communication bandwidth between CPU and GPU needs to be drastically improved .
This paper discusses a Distributed Compressed Video Sensing ( DCVS ) framework using Convolutional Sparse Coding ( CSC ) . CSC is a technique to represent a signal as convolutions of filters and corresponding coefficients . Conventional block based DCVS methods divide a given video sequence into key and non-key frames .
The implicitly multi-scale fusion scheme is then incorporated into bidirectional recurrent convolutional neural network , which aims to iteratively model spatial relations between horizontally or vertically adjacent sub-aperture images of light-field data . Within the network , the recurrent convolutions are modified to be more effective and flexible in modeling the spatial correlations between neighboring views . A horizontal sub-network and a vertical sub-network of the same network structure are ensembled for final outputs via stacked generalization .
Deep Convolutional Neural Networks ( CNNs ) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) since 2012 , yet there is relatively no clear understanding of the reasons behind their outstanding performance , or how they might be improved , In this paper we present a novel benchmarking of multiple state-of-the-art deep CNN architectures by providing an analysis of important performance metrics : speed , memory consumption , and network parameters utilization . Key findings are : ( 1 ) fully connected layers are of a high cost on speed and memory consumption compared to the sparsely connected layers ; ( 2 ) the depth and the performance of an architecture are in a nonlinear relationship and constrained by layers transformation types ; ( 3 ) 1 × 1 convolutions are an efficient way to reduce dimensionality and pooling features ; ( 4 ) addition units as in residual and densely connected networks accelerate the backpropagation time by distributing the gradients through the graph , we believe our set of benchmarks are a step towards better realization of the best architectural design choices of Deep CNNs .
In this paper , we propose a method of improving temporal Convolutional Neural Networks ( CNN ) by determining the optimal alignment of weights and inputs using dynamic programming . Conventional CNN convolutions linearly match the shared weights to a window of the input . However , it is possible that there exists a more optimal alignment of weights .
In this paper , we integrate the fast Fourier transform ( FFT ) method into the McLaughlin ’s framework , and present an improved FFT-based Montgomery modular multiplication ( MMM ) algorithm achieving high area-time efficiency . Compared to the previous FFT-based designs , we inhibit the zero-padding operation by computing the modular multiplication steps directly using cyclic and nega-cyclic convolutions . Thus , we reduce the convolution length by half .
Note the original work only defined the model for . Molecular Graph Convolutions , ( ) This work deviates slightly from other MPNNs in that it introduces edge representations which are updated during the message passing phase .
These input representations include the Coulomb Matrix ( CM , ) , Bag of Bonds ( BoB , ) , Bonds Angles , Machine Learning ( BAML , ) , Extended Connectivity Fingerprints ( ECPF4 , ) , and “ Projected Histograms ” ( HDAD , ) representations . In addition to these hand engineered features we include two existing baseline MPNNs , the Molecular Graph Convolutions model ( GC ) from , and the original GG - NN model trained with distance bins . Overall , our new MPNN achieves chemical accuracy on 11 out of 13 targets and state of the art on all 13 targets .
( 2016 ) have shown that text classification accuracy increases when the proposed model becomes deeper . VDCNN uses only small kernel convolutions and pooling operations . The proposed architecture relies on the VGG and ResNet philosophy : The number of feature maps and the temporal resolution is modeled so that their product is constant .
Each kernel thus acts as the ultimate detector of tissue from one of the segmentation labels . We use the softmax non - linearity which normalizes the result of the kernel convolutions into a multinominal distribution over the labels . Specifically , let be the vector of values at a given spatial position , it computes where is a normalization constant .
One can see similar effects when using linear activations for the dimension reduction components . subsection : Spatial Factorization into Asymmetric Convolutions The above results suggest that convolutions with filters larger a might not be generally useful as they can always be reduced into a sequence of convolutional layers . Still we can ask the question whether one should factorize them into smaller , for example convolutions . However , it turns out that one can do even better than by using asymmetric convolutions , e.g. . For example using a convolution followed by a convolution is equivalent to sliding a two layer network with the same receptive field as in a convolution ( see figure [ reference ] ) .
" I " is input image , " conv " backbone convolutions , " pool " region - wise feature extraction , " H " network head , " B " bounding box , and " C " classification . " B0 " is proposals in all architectures .
section : Experiments We design our experiments with the goal to answer two key questions : What is the performance impact of replacing convolutions in a ByteNet - like model with depthwise separable convolutions ? What is the performance trade - off of reducing dilation while correspondingly increasing convolution window size ?
The input - to - state convolution in this layer uses a mask of type B , whereas the state - to - state convolution is not masked . The PixelCNN uses convolutions of size with a mask of type B. The top feature map is then passed through a couple of layers consisting of a Rectified Linear Unit ( ReLU ) and a convolution .
However , none of these approaches has been demonstrated improvements over state of the art results on large benchmark datasets . Gated convolutions have been previously explored for machine translation by [ reference ] but their evaluation was restricted to a small dataset and the model was used in tandem with a traditional count - based model . Architec -
For this reason , Bo et al . use the Nyström method for their hierarchical kernel descriptors . In this section , we show that when the coordinate sets are two - dimensional regular grids , a natural approximation for the multilayer convolutional kernel consists of a sequence of spatial convolutions with learned filters , pointwise non - linearities , and pooling operations , as illustrated in Figure [ reference ] . More precisely , our scheme approximates the kernel map of defined in ( [ reference ] ) at layer by finite - dimensional spatial maps , where is a set of coordinates related to , and is a positive integer controlling the quality of the approximation .
we make use of small convolutional layers with which can greatly reduce the number of parameters in a network and thus serve as a form of regularization . Additionally , to unify the architecture further , we make use of the fact that if the image area covered by units in the topmost convolutional layer covers a portion of the image large enough to recognize its content ( i.e. the object we want to recognize ) then fully connected layers can also be replaced by simple 1 - by - 1 convolutions . This leads to predictions of object classes at different positions which can then simply be averaged over the whole image .
Concatenated features are smoothed by a convolution with ReLU . Final heatmap which has layers obtained via convolutions without activation . The final output is multiplied with a binary mask of which has in the area of the persons without annotation .
In this paper we study a fundamental question : How much memory is actually needed to train a neural network ? To answer this question , we profile the overall memory usage of training on two representative deep learning benchmarks -- the WideResNet model for image classification and the DynamicConv Transformer model for machine translation -- and comprehensively evaluate four standard techniques for reducing the training memory requirements : ( 1 ) imposing sparsity on the model , ( 2 ) using low precision , ( 3 ) microbatching , and ( 4 ) gradient checkpointing . We explore how each of these techniques in isolation affects both the peak memory usage of training and the quality of the end model , and explore the memory , accuracy , and computation tradeoffs incurred when combining these techniques . Using appropriate combinations of these techniques , we show that it is possible to the reduce the memory required to train a WideResNet-28 - 2 on CIFAR-10 by up to 60.7x with a 0.4 % loss in accuracy , and reduce the memory required to train a DynamicConv model on IWSLT'14 German to English translation by up to 8.7x with a BLEU score drop of 0.15 .
Dynamic Convolution: Attention Over Convolution Kernels Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm A Dynamic Convolutional Layer for short rangeweather prediction A New General Linear Convolution Model for fMRI Data Process A New General Linear Convolution Model for fMRI Data Process Underwater nuclide identification strategy using a multi-agent system with a dedicated scattering and attenuation agent Tractogram filtering of anatomically non-plausible fibers with geometric deep learning Adaptive Sampling Design for Spatio-Temporal Prediction Striving for Simplicity: The All Convolutional Net Quasi-Recurrent Neural Networks Enhanced image classification with a fast-learning shallow convolutional neural network Multi-Scale Context Aggregation by Dilated Convolutions Convolutional Neural Network Architectures for Matching Natural Language Sentences Soft Conditional Computation CondConv: Conditionally Parameterized Convolutions for Efficient Inference Automatic salt deposits segmentation: A deep learning approach An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution Segmentation-Based Method Combined with Dynamic Programming for Brain Midline Delineation Deep feature fusion for self-supervised monocular depth prediction A Regularity Result for Quasilinear Stochastic Partial Differential Equations of Parabolic Type Folding spatial image filters on the CM-5 2-D FIR filter design from independent "Small" generating kernels using a mean square and Tchebyshev error criterion Reinventing 2D Convolutions for 3D Medical Images Reinventing 2D Convolutions for 3D Images. ROI-Attention Vectorized CNN Model for Static Facial Expression Recognition On the use of small 2d convolutions on GPUs Distributed compressed video sensing based on convolutional sparse coding LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution Benchmark analysis of popular ImageNet classification deep CNN architectures Dynamic Weight Alignment for Temporal Convolutional Neural Networks Area-Time Efficient Architecture of FFT-Based Montgomery Multiplication Neural Message Passing for Quantum Chemistry Squeezed Very Deep Convolutional Neural Networks for Text Classification Brain tumor segmentation with Deep Neural Networks Rethinking the Inception Architecture for Computer Vision Cascade R-CNN: Delving Into High Quality Object Detection Depthwise Separable Convolutions for Neural Machine Translation Pixel Recurrent Neural Networks Convolutional Sequence to Sequence Learning Convolutional Kernel Networks Striving for Simplicity: The All Convolutional Net MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Low-Memory Neural Network Training: A Technical Report
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CVPR Spatio-Temporal Design: Advances in Efficient Data Acquisition ICLR ICLR 2015 International Joint Conference on Neural Networks (IJCNN) ICLR NIPS NeurIPS IPPS ICASSP ISCA Workshops ISPACS ICML ICANN Medical Image Anal. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICLR ICML ICML NIPS ICLR ECCV
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