Ultimately here want a fast adder for excessive bit edition . In this paper , they carried out of linear convolution are based on ripple carry adder and array multiplier . Offering common Boolean common sense ( CBL ) adder presents much less additives , less path delay and better pace compare to different present CBL adder and different adders . In digital halftoning it is often useful to adapt the spectral properties of the halftoning algorithm to the local properties of the input image , e.g. its local grayvalue . In an iterative convolution algorithm this leads to the application of a space-variant impulse response in the halftoning process . Experiments demonstrate the properties and the control of this algorithm . With the advent of sound field simulator , many sound fields have been reproduced by obtaining the impulse responses of specific acoustic spaces like famous concert hall , opera house . This sound field reproduction has been done by the linear convolution operation between the sound input signal and the impulse response of certain acoustic space . However , the conventional finite impulse response based linear convolution operation always makes real-time implementation of sound field generator impossible due to the large amount of computational burden . FIR digital filter is essentially a kind of linear convolution operation . When the step N of digital filter is very large , the calculation is also troublesome and the system can not achieve the real-time request . An example of application of Markov model to economic problem is considered . The models of optimal choice were studied for various decision-making strategies , namely , the threshold criterion method , the worst criterion method , additive convolution , and the distance method for different selection functions . Theoretical studies are accompanied by numerical calculations . A system of indicators has been identified for the assessment of the value of interfirm relations , taking into account quantitative and qualitative aspects of partnership . An integrated assessment of the value of interfirm relationship with a partner has been made based on the additive convolution model . Then it is extended to multidimensional problems . The presented generalized convolution concept is based on the earlier A-convolution . This idea is recalled at the beginning and a set of techniques for studying the dependence of the respective coefficients on the arbitrary transform , is suggested . Many images are constructed by active convolution arising from scanning motion . In others , even though motion is absent , the dependence of the image on the object can be expressed as a convolution integral , at least approximately . Thereafter , the signal is not the sound is encoded using an audio codec . At the decoder , there is no ringing signal unit being restored by using the audio codec , with preferably echo field that room ( room ) impulse response generated based on the information by a convolution ( convolution ) , applying the echo in accordance with the information on the echo field by without the restored sound signal unit is substantially converted into the original audio signal . According to the invention , the accompanying audio codec according to encode only audio signals without a ring , solves the problem of a parameterized audio codec to provide a poor performance on echo audio signal . The time-domain forward scattered field defines an impulse response . Energy conservation shows that this impulse response is the kernel of a passive convolution operator , which implies that the Fourier transform of the impulse response is a Herglotz function . The forward scattering sum rule is finally constructed from integral identities for Herglotz functions . A basic result from Network Calculus is that the network service curve is obtained by the min -- plus convolution of the node service curves . We state a theorem on the min -- plus convolution in this work , which simplifies the computation of the min -- plus convolution of service curves . The theorem follows from the continuous ΓΔ-transform , which we develop . Then we combined this attention score with recurrent convolution-based encoded text features to get final sentence representation . Here attention will focus on important text features and recurrent convolution makes full use of limited contextual information by processing sentence representation through different windows sizes with specialized recurrent convolution operation . Validation of the proposed model is done through experimentation with three benchmark datasets i.e. MR , SSTl , and SST2 . We expect that the same kinds of structures can appear at all positions in an input image , so it is reasonable to treat all positions equally by filtering them in the same way . In this way , a bank of neurons in a CNN applies a convolution operation to its input . A single layer in a CNN typically has multiple banks of neurons , each performing a convolution with a different filter . Thus the local visual cues for fine - grained discrimination is enhanced . In addition , through embedding contextual features layer - by - layer ( convolution operation across layers ) , MSCAN can obtain more context - aware representation for input image . To solve the second problem , instead of using rigid body parts , we propose to localize latent pedestrian parts through Spatial Transform Networks ( STN ) , which is originally proposed to learn image transformation . These tasks are difficult ( if not infeasible ) for STN . Active Convolution [ ] This work is contemporary . Then , similar to , a cascade of convolutional layers are applied to perform the non - linear mapping . Each layer is composed of three types of operations , including Convolution ( Conv ) , Rectified Linear Units ( ReLU ) , and Batch Normalization ( BN ) . Specifically , “ Conv + BN + ReLU ” is adopted for each convolutional layer except the last convolutional layer which consists of a single “ Conv ” operation . Following a recent proposition for convolution of complex numbers and quaternions , this paper presents basic neural networks convolution operations using quaternion algebra . The convolution process is defined in the real - valued space by convolving a filter matrix with a vector . In a QCNN , the convolution of a quaternion filter matrix with a quaternion vector is performed . Here t=2 ( 0 ~ 2 ) , refers to the recurrent convolutional operation that includes one single convolution layer followed by two subsequential recurrent convolutional layers . In this implementation , we have applied concatenation to the feature maps from the encoding unit to the decoding unit for both RUNet and R2U - Net models . Designing Efficient Convolutional Neural Networks for Image Classification . Shift operation is an efficient alternative over depthwise separable convolution . However , it is still bottlenecked by its implementation manner , namely memory movement . In order to enable convolution neural network ( CNN ) to be deployed on a Field Programmable Gate Array ( FPGA ) , this study builds a lightweight convolutional neural network that can be separated by a depth to reduce the amount of parameters and computations stored . We replaced the standard convolution operation with a separate convolution operation , and proposed a hardware accelerator architecture that can handle differently sized depth-separable convolution operations , using parallelization to efficiently utilize hardware resources for depth separable convolution . Therefore , data can be reused to reduce number of memory accesses . Abstract Long short-term memory networks ( LSTM ) and classical convolutional neural networks ( CNN ) are two critical methods for the task of target entities sentiment analysis , but LSTM are difficult to parallelize and time-inefficient , and classical CNN can only capture local semantic features . To this end , this paper first proposes a sparse attention based separable dilated convolutional neural network ( SA-SDCCN ) , which consists of multichannel embedding layer , separable dilated convolution module , sparse attention layer , and output layer . Specifically , our work is mainly concentrated on the first three parts . To build richer representations over the input sequence , in multichannel embedding layer , semantic and sentiment embeddings are incorporated into an embedding tensor . In separable dilated convolution module , long-range contextual semantic information is explored and multi-scale contextual semantic dependencies are aggregated simultaneously . In sparse attention layer , sentiment-oriented components are noticed according to the features of specific target entity . 3D medical image classification is a challenging task because of the indistinct tissue characteristics and computational efficiency . To address the challenge , a deep dilated depthwise separable convolutional neural network is proposed in this research . A depthwise separable architecture is introduced to improve parameter utilization efficiency . However , the simple combination of these two methods results in an over-simplified operation which causes severe performance degradation due to loss of information contained in the feature map . To resolve this problem , we propose a new block called Concentrated-Comprehensive Convolution ( C3 ) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution . The C3 block consists of a concentration stage and a comprehensive convolution stage . We propose a novel " spark " module which is a combination of the " fire " module of SqueezeNet and depth-wise separable convolutions . Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder . The resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN ) . For investigating the beneficial effect of channel-wise feature on improving details representation ability , we design a Hybrid Pyramid Pooling(HPP ) structure to explicitly exploit the channel-wise features . Through utilizing different scales of depthwise dilated convolution , segmentation networks can fully utilize the superiority of channel-wise features to improve the details representation ability . For deeply developing the advantages of the channel-wisely calculated features by HPP , we design another Stacked Pyramid Structure(SPS ) , which contains stacked pyramid pooling structures . Then multi-context including both local and global context is added to these multi-scale features without extra computational burden . The local context is added through a depthwise separable convolution based approach , and the global context by a simple global average pooling way . Experimental results show that our method can run at about 110 fps on VGA ( Video Graphics Array)-resolution images , while still maintaining competitive precision on WIDER FACE and FDDB ( Face Detection Data Set and Benchmark ) datasets as compared with its state-of-the-art counterparts . using this parametrization of filters ( 13 ) in the convolutional layer is applied to the m×n matrix X ( single input channel ) , producing q outputs ( i.e. , a tensor of size m × n × q ) . Separable convolution . A simplification of the multigraph convolution is obtained considering the factorized form of the matrix X = WH and applying onedimensional convolution on the respective graph to each factor , w Assume we have channels and filters ( often or more ) and a receptive field of size ( often but we will use upto ) . The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions SPP and Fast R - CNN address this by applying a ConvNet fully convolutionally on the image and extracting features directly from the feature maps for each RoI. Faster R - CNN further improves efficiency by replacing the low - level vision algorithm with a region proposal network . R - FCN replaces the expensive fully connected sub - detection network with a fully convolutional network , and Light - Head R - CNN reduces the cost in R - FCN by applying separable convolution to reduce the number of channels in the feature maps before RoI pooling . On the other hand , one - stage detectors remove the region pooling step of two - stage detectors . Most approaches consist in compressing pre - trained networks or training small networks directly . A recent tendency in deep models is replacing standard convolutional blocks with Depthwise Separable Convolutions ( DSCs ) . The purpose is to reduce the number of parameters and consequently the model size . This method restores the image by combining sample representations and sample distribution models which respectively based on self-encoder reconstruction loss learning and generative adversarial networks . In this method , the infrastructure is a conditional generative adversarial network , the condition is the images that are from the other perspectives , and the generator is a self-encoder structure with cross-layer connection , group convolution and feature map channel exchanged . This method was carried out on a dataset recorded in Zurich using a pair of cameras mounted on a mobile platform . Shortcut shift requires no time to realize residual connection through allocating space in advance . We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet . Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy . Previous works typically reduce computations from the structure level . For example , group convolution based methods reduce computations by factorizing a vanilla convolution into depth-wise and point-wise convolutions . Pruning based methods prune redundant connections in the network structure . The proposed network consists of a feature extraction block , N channel-fusion enhancement blocks ( CFEBs ) and an upscale block . In order to reduce model parameters and computation complexity , each CFEB adopts group convolution operation and channel-fusion strategy to fuse image information from different channels . In addition , we propose a global-relation attention block to enable interactions among groups and an enhancement block to better extract and fuse multi-scale feature information . In this paper , we are interested in boosting the representation capability of convolution neural networks which utilizing the inverted residual structure . Based on the success of Inverted Residual structure[Sandler et al. 2018 ] and Interleaved Low-Rank Group Convolutions[Sun et al. 2018 ] , we rethink this two pattern of neural network structure , rather than NAS(Neural architecture search ) method[Zoph and Le 2017 ; Pham et al. 2018 ; Liu et al. 2018b ] , we introduce uneven point-wise group convolution , which provide a novel search space for designing basic blocks to obtain better trade-off between representation capability and computational cost . Meanwhile , we propose two novel information flow patterns that will enable cross-group information flow for multiple group convolution layers with and without any channel permute/shuffle operation . Deep neural decision forests [ reference ] are tree - patterned multi - branch networks with learned splitting functions . Grouped convolutions . The use of grouped convolutions dates back to the AlexNet paper [ reference ] , if not earlier . Our module requires minimal extra effort designing each path . Relation to Grouped Convolutions . The above module becomes more succinct using the notation of grouped convolutions [ reference ] . [ reference ] Performance . For simplicity we use Torch 's built - in grouped convolution implementation , without special optimization . We note that this implementation was brute - force and not parallelization - friendly . Such an architecture has been previously shown to perform well for image classification . We also experimented with using grouped convolutions ( or " sub - separable convolutions " ) and add even more separation with our new super - separable convolutions . We do away with filter dilation in our architecture , after exploring the trade - off between filter dilation and larger convolution windows . Assume we have channels and filters ( often or more ) and a receptive field of size ( often but we will use upto ) . The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions The Volume Local Phase Quantization ( VLPQ ) method introduced is based on binary encoding of the phase information of the local Fourier transform at low frequency points and is an extension to the LPQ operator used for spatial texture analysis . The local Fourier transform is computed efficiently using 1-D convolutions for each dimension in a 3-D volume . The data achieved is compressed to a smaller dimension before a scalar quantization procedure . Firstly , a full-hardware architecture based on parallel pipeline was designed to achieve high throughput rate . Secondly , in order to save some multiplier , the 2-D convolution is separated into two 1-D convolutions . Thirdly , a dedicate first in first out memory named as CAFIFO ( Column Addressing FIFO ) was designed to avoid the error propagating induced by spark on clock . Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks ( ConvNet ) on various hardware platforms because it requires fewer floating point operations than FFT-based or direct convolutions . This paper compares three highly optimized implementations ( regular FFT-- , Gauss -- FFT-- , and Winograd -- based convolutions ) on modern multi-- and many -- core CPUs . Although all three implementations employed the same optimizations for modern CPUs , our experimental results with two popular ConvNets ( VGG and AlexNet ) show that the FFT -- based implementations generally outperform the Winograd -- based approach , contrary to the popular belief . Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks ( ConvNet ) on various hardware platforms because it could require fewer floating point operations than FFT-based or direct convolutions . In this paper , we analyze the theoretical performances of three methods ( regular FFT- , Gauss-FFT- , and Winograd-based convolutions ) , as well as compare their highly optimized implementations on modern multi- and many-core CPUs . With all three implementations employing the same optimizations on modern CPUs , our experimental results with modern ConvNets show that the FFT-based implementations generally outperform the Winograd-based approach , which is contrary to the popular belief . Two dimensional ( 2-D ) convolutions are local by nature ; hence every pixel in the output image is computed using surrounding information , i.e. a moving window of pixels . Although the operation is simple , the hardware is conditioned by the fact that due to bandwidth efficiency full raster rows must be read from the external memory , and that a row-major image scan should be performed to support shift-variant convolutions . in this paper , a new combined Fast Polynomial Transform and Fermat Number Transform ( FPT-FNT ) algorithm is proposed for computing two-dimensional cyclic convolutions . It is shown that this algorithm is very efficient , it requires only NxN multiplications for computing two-dimensional cyclic convolution of two NxN sequences . This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM , based on our previous gesture recognition architectures that combine the 3-D convolutional neural network ( CNN ) and ConvLSTM . Depthwise separable , group , and shuffle convolutions are used to replace the convolutional structures in ConvLSTM for the redundancy analysis . In addition , four ConvLSTM variants are derived for attention analysis : 1 ) by removing the convolutional structures of the three gates in ConvLSTM ; 2 ) by applying the attention mechanism on the ConvLSTM input ; and 3 ) by reconstructing the input and 4 ) output gates with the modified channelwise attention mechanism . The harmonic analysis provides new special functions , orthogonal bases , PDEs , ODEs and integral equations . The tools to be developed rely on group representations , continuous and discrete wavelets , the estimation theory ( prediction , smoothing and interpolation ) and filtering theory ( Kalman filters , motion-based convolutions , integral transforms ) . All the algorithms are supported by fast and parallelizable implementations based on the FFT and dynamic programming . The model combines a convolutional processing layer with a bidirectional analysis module . This way , RGB information is efficiently handled by the hierarchies of convolutions , while the bidirectional properties of a long short term memory ( LSTM ) model are retained . The resulting convolutionally enhanced bidirectional LSTM ( CEBi-LSTM ) architecture is less sensitive to skeleton errors , occurring using low-cost sensors , such as Kinect , while simultaneously handling the high amount of detail when using RGB visual information . Just like for linear convolution a morphological convolution is specified by a kernel and this kernel is what is being optimized during the training process . We demonstrate how the common CNN operations of max/min-pooling and ReLUs arise naturally from solving a PDE and how they are subsumed by morphological convolutions . We present a proof-of-concept experiment to demonstrate the potential of this framework in increasing the performance of deep learning based imaging applications . Chapters 3 and 4 , and their extensions in Chapters 7and 8 , form the core of the text . The third chapter addresses fast algorithms for short convolutions . The Cook-Toom convolution algorithm is discussed , followed by the Winograd convolution algorithm . Fourier-based homogenization schemes are useful to analyze heterogeneous microstructures represented by 2D or 3D image data . These iterative schemes involve discrete periodic convolutions with global ansatz functions ( mostly fundamental solutions ) . The convolutions are efficiently computed using the fast Fourier transform . section : Results For natural images , all models except otherwise specified were single - layer , with , a kernel size of , and stride 2 convolutions between input layers and hidden layers with latent feature maps . We trained the models on Cifar - 10 and ImageNet with and LSTM feature maps respectively . The fully connected layers of these nets have fixed dimensions and throw away spatial coordinates . However , fully connected layers can also be viewed as convolutions with kernels that cover their entire input regions . Doing so casts these nets into fully convolutional networks that take input of any size and make spatial output maps . The topology of the hourglass is symmetric , so for every layer present on the way down there is a corresponding layer going up . After reaching the output resolution of the network , two consecutive rounds of 1x1 convolutions are applied to produce the final network predictions . The output of the network is a set of heatmaps where for a given heatmap the network predicts the probability of a joint ’s presence at each and every pixel . The proposed convolutional sentence model takes simple architectures such as ( essentially the same convolutional architecture as SENNA ) , which consists of a convolution layer and a max - pooling over the entire sentence for each feature map . This type of models , with local convolutions and a global pooling , essentially do a “ soft ” local template matching and is able to detect local features useful for a certain task . Since the sentence - level sequential order is inevitably lost in the global pooling , the model is incapable of modeling more complicated structures . To understand the role of skip connections , we analyze and compare various types of . We find that the identity mapping chosen in achieves the fastest error reduction and lowest training loss among all variants we investigated , whereas skip connections of scaling , gating , and 1 1 convolutions all lead to higher training loss and error . These experiments suggest that keeping a “ clean ” information path ( indicated by the grey arrows in Fig . In the original RPN design , a small subnetwork is evaluated on dense 3×3 sliding windows , on top of a singlescale convolutional feature map , performing object / nonobject binary classification and bounding box regression . This is realized by a 3×3 convolutional layer followed by two sibling 1×1 convolutions for classification and regression , which we refer to as a network head . The object / nonobject criterion and bounding box regression target are defined with respect to a set of reference boxes called anchors
Ultimately here want a fast adder for excessive bit edition . In this paper , they carried out of linear convolution are based on ripple carry adder and array multiplier . Offering common Boolean common sense ( CBL ) adder presents much less additives , less path delay and better pace compare to different present CBL adder and different adders .
In digital halftoning it is often useful to adapt the spectral properties of the halftoning algorithm to the local properties of the input image , e.g. its local grayvalue . In an iterative convolution algorithm this leads to the application of a space-variant impulse response in the halftoning process . Experiments demonstrate the properties and the control of this algorithm .
With the advent of sound field simulator , many sound fields have been reproduced by obtaining the impulse responses of specific acoustic spaces like famous concert hall , opera house . This sound field reproduction has been done by the linear convolution operation between the sound input signal and the impulse response of certain acoustic space . However , the conventional finite impulse response based linear convolution operation always makes real-time implementation of sound field generator impossible due to the large amount of computational burden .
FIR digital filter is essentially a kind of linear convolution operation . When the step N of digital filter is very large , the calculation is also troublesome and the system can not achieve the real-time request .
An example of application of Markov model to economic problem is considered . The models of optimal choice were studied for various decision-making strategies , namely , the threshold criterion method , the worst criterion method , additive convolution , and the distance method for different selection functions . Theoretical studies are accompanied by numerical calculations .
A system of indicators has been identified for the assessment of the value of interfirm relations , taking into account quantitative and qualitative aspects of partnership . An integrated assessment of the value of interfirm relationship with a partner has been made based on the additive convolution model .
Then it is extended to multidimensional problems . The presented generalized convolution concept is based on the earlier A-convolution . This idea is recalled at the beginning and a set of techniques for studying the dependence of the respective coefficients on the arbitrary transform , is suggested .
Many images are constructed by active convolution arising from scanning motion . In others , even though motion is absent , the dependence of the image on the object can be expressed as a convolution integral , at least approximately .
Thereafter , the signal is not the sound is encoded using an audio codec . At the decoder , there is no ringing signal unit being restored by using the audio codec , with preferably echo field that room ( room ) impulse response generated based on the information by a convolution ( convolution ) , applying the echo in accordance with the information on the echo field by without the restored sound signal unit is substantially converted into the original audio signal . According to the invention , the accompanying audio codec according to encode only audio signals without a ring , solves the problem of a parameterized audio codec to provide a poor performance on echo audio signal .
The time-domain forward scattered field defines an impulse response . Energy conservation shows that this impulse response is the kernel of a passive convolution operator , which implies that the Fourier transform of the impulse response is a Herglotz function . The forward scattering sum rule is finally constructed from integral identities for Herglotz functions .
A basic result from Network Calculus is that the network service curve is obtained by the min -- plus convolution of the node service curves . We state a theorem on the min -- plus convolution in this work , which simplifies the computation of the min -- plus convolution of service curves . The theorem follows from the continuous ΓΔ-transform , which we develop .
Then we combined this attention score with recurrent convolution-based encoded text features to get final sentence representation . Here attention will focus on important text features and recurrent convolution makes full use of limited contextual information by processing sentence representation through different windows sizes with specialized recurrent convolution operation . Validation of the proposed model is done through experimentation with three benchmark datasets i.e. MR , SSTl , and SST2 .
We expect that the same kinds of structures can appear at all positions in an input image , so it is reasonable to treat all positions equally by filtering them in the same way . In this way , a bank of neurons in a CNN applies a convolution operation to its input . A single layer in a CNN typically has multiple banks of neurons , each performing a convolution with a different filter .
Thus the local visual cues for fine - grained discrimination is enhanced . In addition , through embedding contextual features layer - by - layer ( convolution operation across layers ) , MSCAN can obtain more context - aware representation for input image . To solve the second problem , instead of using rigid body parts , we propose to localize latent pedestrian parts through Spatial Transform Networks ( STN ) , which is originally proposed to learn image transformation .
These tasks are difficult ( if not infeasible ) for STN . Active Convolution [ ] This work is contemporary .
Then , similar to , a cascade of convolutional layers are applied to perform the non - linear mapping . Each layer is composed of three types of operations , including Convolution ( Conv ) , Rectified Linear Units ( ReLU ) , and Batch Normalization ( BN ) . Specifically , “ Conv + BN + ReLU ” is adopted for each convolutional layer except the last convolutional layer which consists of a single “ Conv ” operation .
Following a recent proposition for convolution of complex numbers and quaternions , this paper presents basic neural networks convolution operations using quaternion algebra . The convolution process is defined in the real - valued space by convolving a filter matrix with a vector . In a QCNN , the convolution of a quaternion filter matrix with a quaternion vector is performed .
Here t=2 ( 0 ~ 2 ) , refers to the recurrent convolutional operation that includes one single convolution layer followed by two subsequential recurrent convolutional layers . In this implementation , we have applied concatenation to the feature maps from the encoding unit to the decoding unit for both RUNet and R2U - Net models .
Designing Efficient Convolutional Neural Networks for Image Classification . Shift operation is an efficient alternative over depthwise separable convolution . However , it is still bottlenecked by its implementation manner , namely memory movement .
In order to enable convolution neural network ( CNN ) to be deployed on a Field Programmable Gate Array ( FPGA ) , this study builds a lightweight convolutional neural network that can be separated by a depth to reduce the amount of parameters and computations stored . We replaced the standard convolution operation with a separate convolution operation , and proposed a hardware accelerator architecture that can handle differently sized depth-separable convolution operations , using parallelization to efficiently utilize hardware resources for depth separable convolution . Therefore , data can be reused to reduce number of memory accesses .
Abstract Long short-term memory networks ( LSTM ) and classical convolutional neural networks ( CNN ) are two critical methods for the task of target entities sentiment analysis , but LSTM are difficult to parallelize and time-inefficient , and classical CNN can only capture local semantic features . To this end , this paper first proposes a sparse attention based separable dilated convolutional neural network ( SA-SDCCN ) , which consists of multichannel embedding layer , separable dilated convolution module , sparse attention layer , and output layer . Specifically , our work is mainly concentrated on the first three parts . To build richer representations over the input sequence , in multichannel embedding layer , semantic and sentiment embeddings are incorporated into an embedding tensor . In separable dilated convolution module , long-range contextual semantic information is explored and multi-scale contextual semantic dependencies are aggregated simultaneously . In sparse attention layer , sentiment-oriented components are noticed according to the features of specific target entity .
3D medical image classification is a challenging task because of the indistinct tissue characteristics and computational efficiency . To address the challenge , a deep dilated depthwise separable convolutional neural network is proposed in this research . A depthwise separable architecture is introduced to improve parameter utilization efficiency .
However , the simple combination of these two methods results in an over-simplified operation which causes severe performance degradation due to loss of information contained in the feature map . To resolve this problem , we propose a new block called Concentrated-Comprehensive Convolution ( C3 ) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution . The C3 block consists of a concentration stage and a comprehensive convolution stage .
We propose a novel " spark " module which is a combination of the " fire " module of SqueezeNet and depth-wise separable convolutions . Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder . The resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN ) .
For investigating the beneficial effect of channel-wise feature on improving details representation ability , we design a Hybrid Pyramid Pooling(HPP ) structure to explicitly exploit the channel-wise features . Through utilizing different scales of depthwise dilated convolution , segmentation networks can fully utilize the superiority of channel-wise features to improve the details representation ability . For deeply developing the advantages of the channel-wisely calculated features by HPP , we design another Stacked Pyramid Structure(SPS ) , which contains stacked pyramid pooling structures .
Then multi-context including both local and global context is added to these multi-scale features without extra computational burden . The local context is added through a depthwise separable convolution based approach , and the global context by a simple global average pooling way . Experimental results show that our method can run at about 110 fps on VGA ( Video Graphics Array)-resolution images , while still maintaining competitive precision on WIDER FACE and FDDB ( Face Detection Data Set and Benchmark ) datasets as compared with its state-of-the-art counterparts .
using this parametrization of filters ( 13 ) in the convolutional layer is applied to the m×n matrix X ( single input channel ) , producing q outputs ( i.e. , a tensor of size m × n × q ) . Separable convolution . A simplification of the multigraph convolution is obtained considering the factorized form of the matrix X = WH and applying onedimensional convolution on the respective graph to each factor , w
Assume we have channels and filters ( often or more ) and a receptive field of size ( often but we will use upto ) . The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions
SPP and Fast R - CNN address this by applying a ConvNet fully convolutionally on the image and extracting features directly from the feature maps for each RoI. Faster R - CNN further improves efficiency by replacing the low - level vision algorithm with a region proposal network . R - FCN replaces the expensive fully connected sub - detection network with a fully convolutional network , and Light - Head R - CNN reduces the cost in R - FCN by applying separable convolution to reduce the number of channels in the feature maps before RoI pooling . On the other hand , one - stage detectors remove the region pooling step of two - stage detectors .
Most approaches consist in compressing pre - trained networks or training small networks directly . A recent tendency in deep models is replacing standard convolutional blocks with Depthwise Separable Convolutions ( DSCs ) . The purpose is to reduce the number of parameters and consequently the model size .
This method restores the image by combining sample representations and sample distribution models which respectively based on self-encoder reconstruction loss learning and generative adversarial networks . In this method , the infrastructure is a conditional generative adversarial network , the condition is the images that are from the other perspectives , and the generator is a self-encoder structure with cross-layer connection , group convolution and feature map channel exchanged . This method was carried out on a dataset recorded in Zurich using a pair of cameras mounted on a mobile platform .
Shortcut shift requires no time to realize residual connection through allocating space in advance . We blend these shift-based primitives with point-wise group convolution and built two inference-efficient CNN architectures named AddressNet and Enhanced AddressNet . Experiments on CIFAR100 and ImageNet datasets show that our models are faster and achieve comparable or better accuracy .
Previous works typically reduce computations from the structure level . For example , group convolution based methods reduce computations by factorizing a vanilla convolution into depth-wise and point-wise convolutions . Pruning based methods prune redundant connections in the network structure .
The proposed network consists of a feature extraction block , N channel-fusion enhancement blocks ( CFEBs ) and an upscale block . In order to reduce model parameters and computation complexity , each CFEB adopts group convolution operation and channel-fusion strategy to fuse image information from different channels . In addition , we propose a global-relation attention block to enable interactions among groups and an enhancement block to better extract and fuse multi-scale feature information .
In this paper , we are interested in boosting the representation capability of convolution neural networks which utilizing the inverted residual structure . Based on the success of Inverted Residual structure[Sandler et al. 2018 ] and Interleaved Low-Rank Group Convolutions[Sun et al. 2018 ] , we rethink this two pattern of neural network structure , rather than NAS(Neural architecture search ) method[Zoph and Le 2017 ; Pham et al. 2018 ; Liu et al. 2018b ] , we introduce uneven point-wise group convolution , which provide a novel search space for designing basic blocks to obtain better trade-off between representation capability and computational cost . Meanwhile , we propose two novel information flow patterns that will enable cross-group information flow for multiple group convolution layers with and without any channel permute/shuffle operation .
Deep neural decision forests [ reference ] are tree - patterned multi - branch networks with learned splitting functions . Grouped convolutions . The use of grouped convolutions dates back to the AlexNet paper [ reference ] , if not earlier .
Our module requires minimal extra effort designing each path . Relation to Grouped Convolutions . The above module becomes more succinct using the notation of grouped convolutions [ reference ] . [ reference ]
Performance . For simplicity we use Torch 's built - in grouped convolution implementation , without special optimization . We note that this implementation was brute - force and not parallelization - friendly .
Such an architecture has been previously shown to perform well for image classification . We also experimented with using grouped convolutions ( or " sub - separable convolutions " ) and add even more separation with our new super - separable convolutions . We do away with filter dilation in our architecture , after exploring the trade - off between filter dilation and larger convolution windows .
Assume we have channels and filters ( often or more ) and a receptive field of size ( often but we will use upto ) . The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions
The Volume Local Phase Quantization ( VLPQ ) method introduced is based on binary encoding of the phase information of the local Fourier transform at low frequency points and is an extension to the LPQ operator used for spatial texture analysis . The local Fourier transform is computed efficiently using 1-D convolutions for each dimension in a 3-D volume . The data achieved is compressed to a smaller dimension before a scalar quantization procedure .
Firstly , a full-hardware architecture based on parallel pipeline was designed to achieve high throughput rate . Secondly , in order to save some multiplier , the 2-D convolution is separated into two 1-D convolutions . Thirdly , a dedicate first in first out memory named as CAFIFO ( Column Addressing FIFO ) was designed to avoid the error propagating induced by spark on clock .
Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks ( ConvNet ) on various hardware platforms because it requires fewer floating point operations than FFT-based or direct convolutions . This paper compares three highly optimized implementations ( regular FFT-- , Gauss -- FFT-- , and Winograd -- based convolutions ) on modern multi-- and many -- core CPUs . Although all three implementations employed the same optimizations for modern CPUs , our experimental results with two popular ConvNets ( VGG and AlexNet ) show that the FFT -- based implementations generally outperform the Winograd -- based approach , contrary to the popular belief .
Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks ( ConvNet ) on various hardware platforms because it could require fewer floating point operations than FFT-based or direct convolutions . In this paper , we analyze the theoretical performances of three methods ( regular FFT- , Gauss-FFT- , and Winograd-based convolutions ) , as well as compare their highly optimized implementations on modern multi- and many-core CPUs . With all three implementations employing the same optimizations on modern CPUs , our experimental results with modern ConvNets show that the FFT-based implementations generally outperform the Winograd-based approach , which is contrary to the popular belief .
Two dimensional ( 2-D ) convolutions are local by nature ; hence every pixel in the output image is computed using surrounding information , i.e. a moving window of pixels . Although the operation is simple , the hardware is conditioned by the fact that due to bandwidth efficiency full raster rows must be read from the external memory , and that a row-major image scan should be performed to support shift-variant convolutions .
in this paper , a new combined Fast Polynomial Transform and Fermat Number Transform ( FPT-FNT ) algorithm is proposed for computing two-dimensional cyclic convolutions . It is shown that this algorithm is very efficient , it requires only NxN multiplications for computing two-dimensional cyclic convolution of two NxN sequences .
This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM , based on our previous gesture recognition architectures that combine the 3-D convolutional neural network ( CNN ) and ConvLSTM . Depthwise separable , group , and shuffle convolutions are used to replace the convolutional structures in ConvLSTM for the redundancy analysis . In addition , four ConvLSTM variants are derived for attention analysis : 1 ) by removing the convolutional structures of the three gates in ConvLSTM ; 2 ) by applying the attention mechanism on the ConvLSTM input ; and 3 ) by reconstructing the input and 4 ) output gates with the modified channelwise attention mechanism .
The harmonic analysis provides new special functions , orthogonal bases , PDEs , ODEs and integral equations . The tools to be developed rely on group representations , continuous and discrete wavelets , the estimation theory ( prediction , smoothing and interpolation ) and filtering theory ( Kalman filters , motion-based convolutions , integral transforms ) . All the algorithms are supported by fast and parallelizable implementations based on the FFT and dynamic programming .
The model combines a convolutional processing layer with a bidirectional analysis module . This way , RGB information is efficiently handled by the hierarchies of convolutions , while the bidirectional properties of a long short term memory ( LSTM ) model are retained . The resulting convolutionally enhanced bidirectional LSTM ( CEBi-LSTM ) architecture is less sensitive to skeleton errors , occurring using low-cost sensors , such as Kinect , while simultaneously handling the high amount of detail when using RGB visual information .
Just like for linear convolution a morphological convolution is specified by a kernel and this kernel is what is being optimized during the training process . We demonstrate how the common CNN operations of max/min-pooling and ReLUs arise naturally from solving a PDE and how they are subsumed by morphological convolutions . We present a proof-of-concept experiment to demonstrate the potential of this framework in increasing the performance of deep learning based imaging applications .
Chapters 3 and 4 , and their extensions in Chapters 7and 8 , form the core of the text . The third chapter addresses fast algorithms for short convolutions . The Cook-Toom convolution algorithm is discussed , followed by the Winograd convolution algorithm .
Fourier-based homogenization schemes are useful to analyze heterogeneous microstructures represented by 2D or 3D image data . These iterative schemes involve discrete periodic convolutions with global ansatz functions ( mostly fundamental solutions ) . The convolutions are efficiently computed using the fast Fourier transform .
section : Results For natural images , all models except otherwise specified were single - layer , with , a kernel size of , and stride 2 convolutions between input layers and hidden layers with latent feature maps . We trained the models on Cifar - 10 and ImageNet with and LSTM feature maps respectively .
The fully connected layers of these nets have fixed dimensions and throw away spatial coordinates . However , fully connected layers can also be viewed as convolutions with kernels that cover their entire input regions . Doing so casts these nets into fully convolutional networks that take input of any size and make spatial output maps .
The topology of the hourglass is symmetric , so for every layer present on the way down there is a corresponding layer going up . After reaching the output resolution of the network , two consecutive rounds of 1x1 convolutions are applied to produce the final network predictions . The output of the network is a set of heatmaps where for a given heatmap the network predicts the probability of a joint ’s presence at each and every pixel .
The proposed convolutional sentence model takes simple architectures such as ( essentially the same convolutional architecture as SENNA ) , which consists of a convolution layer and a max - pooling over the entire sentence for each feature map . This type of models , with local convolutions and a global pooling , essentially do a “ soft ” local template matching and is able to detect local features useful for a certain task . Since the sentence - level sequential order is inevitably lost in the global pooling , the model is incapable of modeling more complicated structures .
To understand the role of skip connections , we analyze and compare various types of . We find that the identity mapping chosen in achieves the fastest error reduction and lowest training loss among all variants we investigated , whereas skip connections of scaling , gating , and 1 1 convolutions all lead to higher training loss and error . These experiments suggest that keeping a “ clean ” information path ( indicated by the grey arrows in Fig .
In the original RPN design , a small subnetwork is evaluated on dense 3×3 sliding windows , on top of a singlescale convolutional feature map , performing object / nonobject binary classification and bounding box regression . This is realized by a 3×3 convolutional layer followed by two sibling 1×1 convolutions for classification and regression , which we refer to as a network head . The object / nonobject criterion and bounding box regression target are defined with respect to a set of reference boxes called anchors
Review Paper on High Speed Area Efficient Linear Convolution in Different Adder Halftoning with iterative convolution algorithm Fast Convolution Method Using Real-time Masking Effects in Sound Reverberator A Fast Calculate for FIR Digital Filter The use of fuzzy arithmetic in decision-making problems THE METHOD OF EVALUATION OF INTERFIRM RELATIONS IN SUPPLY CHAINS ON THE BASIS OF THE THEORY OF FUZZY SETS TOOLS Generalized convolution as a tool for the multi-dimensional filtering tasks Imaging by Convolution How to code the echo sound signal Time-domain approach to the forward scattering sum rule A transform for network calculus and its application to multimedia networking Recurrent Convolutional Attention Neural Model for Sentiment Classification of short text Improving neural networks by preventing co-adaptation of feature detectors Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Deformable Convolutional Networks Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification FPGA-Based Depth Separable Convolution Neural Network Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis A deep separable neural network for human tissue identification in three-dimensional optical coherence tomography images C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation SegFast: A Faster SqueezeNet based Semantic Image Segmentation Technique using Depth-wise Separable Convolutions Semantic Segmentation with Hybrid Pyramid Pooling and Stacked Pyramid Structure A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks Depthwise Separable Convolutions for Neural Machine Translation CornerNet-Lite: Efficient Keypoint Based Object Detection Squeezed Very Deep Convolutional Neural Networks for Text Classification Multiview Scene Image Inpainting Based on Conditional Generative Adversarial Networks Shift-based Primitives for Efficient Convolutional Neural Networks A closer look at network resolution for efficient network design Image super-resolution via multi-view information fusion networks Seesaw-Net: Convolution Neural Network With Uneven Group Convolution Aggregated Residual Transformations for Deep Neural Networks Depthwise Separable Convolutions for Neural Machine Translation Volume Local Phase Quantization for Blur-Insensitive Dynamic Texture Classification A real-time multi-scale 2D Gaussian filter based on FPGA FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why The anatomy of efficient FFT and winograd convolutions on modern CPUs Area-efficient 2-D shift-variant convolvers for FPGA-based digital image processing Combined FPT-FNT Algorithm for Computing Two-dimensional Cyclic Convolutions Redundancy and Attention in Convolutional LSTM for Gesture Recognition Harmonic analysis associated with spatio-temporal transformations Motion Primitives Classification Using Deep Learning Models for Serious Game Platforms PDE-based Group Equivariant Convolutional Neural Networks Fast algorithms for digital signal processing Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems Towards Conceptual Compression Fully Convolutional Networks for Semantic Segmentation Stacked Hourglass Networks for Human Pose Estimation Convolutional Neural Network Architectures for Matching Natural Language Sentences Identity Mappings in Deep Residual Networks Feature Pyramid Networks for Object Detection
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ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition INTERSPEECH ArXiv ICCE ICVGIP NIPS ICLR ArXiv ICANN 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR SCIA ICS ICASSP NIPS IEEE Transactions on Pattern Analysis and Machine Intelligence ECCV NIPS ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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