In this improved U-net model , the highly modular ResNeXt50 network is used as the encoder of U-net and a parallel dense residual module based on atrous convolution is proposed to extract multi-scale semantic information for segmentation . Moreover , the transposed convolution whose stride is 2 is used for upsampling to obtain the grayscale segmentation mask . This modified model adopts the sum of jaccard loss and binary cross entropy loss as the total loss function . This paper proposes a novel deep learning-based method considering the connectivity of pixels for automatic pavement crack detection which has the potential to complement the current practice involving visual inspection which is costly , inefficient and time-consuming . In the proposed method , the convolutional layers are densely connected in a feed-forward fashion to reuse features from multiple layers , and transposed convolution layers are used for multiple level feature fusion . A novel loss function considering the connectivity of pixels is introduced to overcome the issues related to the output of transposed convolution layers . We use the squeeze-and-excitation residual network as encoder to simultaneously extract features from RGB and depth data and fuse them in the decoder . Multiple average pooling layers and transposed convolution layers are used to process the encoded outputs and fuse their outputs over several decoder layers . To optimize the network parameters , we use a pyramid supervision training scheme , which applies supervised learning over different layers in the decoder to prevent vanishing gradients . The acceleration architecture of transposed convolution layers is essential since transposed convolution operations , as critical components in the generative model of generative adversarial networks , are computationally intensive inherently . In addition , the pre-processing of inserting and padding with zeros for input feature maps causes many ineffective operations . In this paper , we propose a deep neural network model for target classification in automotive radar system . In the proposed network , we introduce transposed convolutional network ( TCNet ) which applies transposed convolution operations . We discuss the properties of transposed convolution and show that TCNet can reduce the network size and improve the classification performance for the systems in which the signals are sparse and memory is restricted like our automotive radar systems . While the FPGA-based CNN accelerators have been widely studied in prior work , the acceleration of deconvolutional networks on FPGA is rarely explored . This paper proposes a novel parametrized deconvolutional architecture based on an FPGA-friendly method , in contrast to the transposed convolution implementation in CPUs and GPUs . Hardware design templates which map this architecture to FPGAs are provided with configurable deconvolutional layer parameters . We initialize the convolutional filters using the method of He et al . . The size of the transposed convolutional filters is and the weights are initialized from a bilinear filter . All the convolutional and transposed convolutional layers ( except the reconstruction layers ) are followed by leaky rectified linear units ( LReLUs ) with a negative slope of 0.2 . Our model progressively predicts high - frequency residuals in a coarse - to - fine manner . By replacing the pre - defined bicubic interpolation with the learned transposed convolutional layers and optimizing the network with a robust loss function , the proposed LapSRN alleviates issues with undesired artifacts and reduces the computational complexity . Extensive evaluations on benchmark datasets demonstrate that the proposed model performs favorably against the state - of - the - art SR algorithms in terms of visual quality and run time . Network Architecture . Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator . For down sampling in the encoding unit , 2×2 max - pooling operations are performed . In the decoding phase , the convolution transpose ( representing up - convolution , or de - convolution ) operations are performed to up - sample the feature maps . The very first version of U - Net was used to crop and copy feature maps from the encoding unit to the decoding unit . In the case of convolution operation , there exist doubly block circulant matrix such that . Consider which is essentially the transpose convolution of on ( ) . Thus , similar to PIM , PICO iterates between the following two steps : Update ; Do transpose convolution of on to get ; update . [ reference ] . In addition , CV ( 128 , 256 , 4c2s ) denotes a convolutional layer with 128 input channels , 256 output channels , and kernel size 4 with stride 2 . Similarly , DC ( 256 , 128 , 4c2s ) denotes a corresponding transposed convolutional operation . Compared to the original DCGAN architecture , the discriminator under our formulation does not have the last sigmoid layer which squashes a scalar value into a probability in [ reference ][ reference ] . Specifically , we use the standard residual block for both self - transitions . A single convolutional layer is used for the down - sampling transition and a single transposed convolutional ( or deconvolutional ) layer is used for the up - sampling transition . The strides in both inter - state layers are set to be the same as the SR upscaling factor . Although a number of pattern recognition methods have been used to analyze the mixed gases , the performances of these methods highly depend on the hand-crafted feature engineering . By contrast , the proposed implementation , based on one-dimensional convolution , is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene , CO , and methane . To the best of our knowledge , the proposed 1D-DCNN algorithm is first applied in the mixture gases ’ recognition . Methods We designed a CNN model and optimized it by dropout and normalization . One-dimensional convolution , max-pooling , and fully-connected multiple perceptron were used to analyze the short-term normal ECG . The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model . The paper investigates systolic array designs for two dimensional convolution and designs for dedicated decimation filters . While existing designs for two dimensional convolution are suitable as decimation filters they can be improved by reducing the number of processing cells . A systolic array for convolution/decimation is presented which completes in time proportional to the image size with the array proportional to the window size . We introduce a novel and generic convolutional unit , DiCE unit , that is built using dimension-wise convolutions and dimension-wise fusion . The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations ; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor . The DiCE unit is simple and can be easily plugged into any architecture to improve its efficiency and performance . First , the energy distributions of autonomous underwater vehicle dynamic signals are given in the time-frequency plane . And then the energy concentration in the energy distribution is enhanced based on a serial signal processing , including wavelet decomposition , modified Bayes ’ classification algorithm , and two dimensional convolution operation , successively . After that the Shannon entropy of the energy distribution is calculated . This paper presents a new formulation of digital filters that combines the description of signal-processing and arithmetic operations . This is done by noting that multiplication is a form of convolution and therefore normal one-dimensional scalar convolution is in fact two-dimensional binary convolution . This is generalized to multidimensions and can be applied with table-look-up and transform techniques . The VSRE is being developed as a research tool for examining multi-modal ( audio-visual ) interactions through the simple manipulation of the acoustic environment and audio parameters ( sound quality ) , that will , through a series of human-based experiments , allow for the testing of the effect of varying these parameters may have on immersion , engagement , and visual fidelity perception within a virtual environment . Finally , we also provide a running time comparison of several one-dimensional convolution implementations . For each of the two directions , the input - to - state component is simply a convolution that contributes to the four gates in the LSTM core ; the operation generates a tensor . The state - to - state recurrent component is then computed with a column - wise convolution that has a kernel of size . The step takes the previous hidden and cell states , combines the contribution of the input - to - state component and produces the next hidden and cell states , as defined in Equation [ reference ] . For all filters , we subtract the mean value over all dimensions in each channel , in order to ensure a mean of zero in each channel . In implementing the two - dimensional convolution operation required for filtering the raw images using , we obtained only the central ‘ valid ’ region , i.e. for images of size , the total dimension of the valid region is . Consequently , the total number of features per image obtained prior to pooling , from filters , and images with channels is . Support vector machines are used to learn from gathered information through relevance feedback . An adaptive convolution kernel is defined to handle the proposed structured multifeature space . The positive definite property of the introduced kernel is proven , as essential condition for uniqueness and optimality of the convex optimization in support vector machines . For the problem that the segmentation of stalk candidates for randomly placed fruit cluster based on existing morphology algorithm tends to be low precision , an improved image segmentation algorithm based on adaptive morphology is proposed . According to edge distances defined based on minimum distance between edge point and unconnected components in minimum domain , the adaptive convolution kernel is constructed . In addition , a run analysis method with different and unordered labels is designed to reduce calculation time of edge distances . In this paper , the structure of SKANet is constructed similarly to that of ResNeXt by repeating residual blocks with the same topology and group convolution for saving computational costs . Different from ResNeXt , the primary convolution is replaced by using selective kernel convolution to adaptively adjust the receptive field based on imported information . Then , attention mechanism is added to the bottom of the block to emphasize on important features and suppress unnecessary ones for more accurate representation of the network . As to the phenomenon that traditional convolutional neural network using a single channel and a single scale convolution kernel to extract image feature leading to low classification accuracy , the cascade convolution module used to extract image feature in this paper . The module uses three different sets of convolution kernels to extract feature . The algorithm has been shown effective when each channel is prime . In this paper , we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism , according to the uncertainty of the convolution kernel size in MBD processing . To reduce the interference of noise , we amend the convolution kernel in an iterative process , and finally restore a clear image . This feature has a greater degree of division in frequency dimension with other target echoes . Two dimensional convolution filtering of frequency domain adaptive convolution kernel and measured echo data is carried out . The filtering results can be used for echo identification of helicopter main rotor . In this paper , the recognition method of no-seedlings grids of tray based on deep convolutional neural network ( GoogLeNet model ) is proposed , which uses 9 Inception-V3 modules to construct deep convolutional neural network . Inception-V3 modules use selective multi-scale convolution kernels to extract grid seedlings features , use the convolution kernels factorization to reduce the operation parameters and use Batch Normalization algorithm to improve the model training speed to build the predictive recognition model of tray no-seedlings grids . To verify the effectiveness of this method , this research designed an images acquisition device , collected 3400 pepper tray grids image samples , label the samples by manual . These improvements , which we demonstrate numerically using a lipid membrane system , arise because the u-series is smooth on the entire real axis and exact up to the cutoff radius . Additional performance improvements over the Ewald decomposition may be possible in certain situations because the far part of the u-series is a sum of Gaussians and can thus be evaluated using algorithms that require a separable convolution kernel ; we describe one such algorithm that reduces communication latency at the expense of communication bandwidth and computation , a trade-off that may be advantageous on modern massively parallel supercomputers . Separable kernel convolution is a fundamental operation for image processing and computer vision applications . Existing parallel implementations of convolution operation convolve a whole image with a separable kernel . In this work , we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage . Our approach transforms a traditional square convolution kernel to line segments , and automatically learn a proper strategy for equipping these line segments to model diverse features . The experimental results indicate that our approach can massively reduce the number of parameters ( pruned 69 % on DenseNet-40 ) and calculations ( pruned 59 % on DenseNet-40 ) while maintaining acceptable performance ( only lose less than 2 % accuracy ) . The output signal error is modelled with the help of a spatio-statistical parameter ( sampling phase ) that depends on the resolution of the input data grid . A frequency domain algorithm is then developed which computes the decay rate of a certain output error functional with respect to the data resolution level , using the power spectra of the input signal , the chosen scaling estimation kernel , and the theoretic convolution kernel of the linear system . A simple numerical experiment is also included to compare the accuracy of the classic FFT approach in SISO approximation problems against the proposed generalization that utilizes an arbitrary reference scaling kernel . Feature maps from different convolution kernels are concatenated as current layer ’s output . To decrease the correlations among different convolution kernels , the dilated convolution is used rather than general convolution kernels . Through this way , multi - scale context knowledge is obtained at the same layer . Then we use four multi - scale convolution layers to obtain the complex image context information . In each multi - scale convolution layer , we use a convolution kernel with size . To obtain multi - scale receptive fields , we adopt dilated convolution for the convolution filters . We assume that ’s are sorted ascendingly and arrange ’s accordingly . The multiplers are the spectral representation of convolution kernel . Denote the set of multipliers as , each corresponds to one . Since many natural functions we care about could be depicted by a small number of low - frequency laplacian eigenbases , we compute and use the smallest eigenvalues as well as the corresponding eigenbases for each in all our experiments . The choice of dilation parameters , number of output channels after each convolution layer , number of learnable parameters in each convolution kernel are shown in Table [ reference ] . We choose in all of our experiments . Non - linearities allow the networks to exploit the full input field , or to focus on fewer elements if needed . section : Each convolution kernel is parameterized as and takes as input X ∈ R k×d which is a concatenation of k input elements embedded in d dimensions and maps them to a single output element Y ∈ R 2d that has twice the dimensionality of the input elements ; subsequent layers operate over the k output elements of the previous layer . Computing a feature map in a convolutional layer ( see Figure [ reference ] ) consists of the following three steps : Convolution of kernels ( filters ) : Each feature map is associated with one kernel ( or several , in the case of Maxout ) . A ReLU activation function is applied after all convolution unless otherwise noted . Once we have the interaction tensor , we use a convolution with kernel to scale down the tensor in a ratio , , without following ReLU . If the input channel is then the output channel is . The proposed novel training scheme is not only computationally efficient but also offers an adaptive way of partially alleviating the inherent class - imbalance of segmentation problems . We analyzed the benefits of using small convolutional kernels in 3D CNNs , which allowed us to develop a deeper and thus more discriminative network , without increasing the computational cost and number of trainable parameters . We discussed the challenges of training deep neural networks and the adopted solutions from the latest advances in deep learning . Since manual segmentation is time-consuming , tedious and error-prone , a fully automatic method for brain tumor segmentation is needed . Recently , state-of-the-art approaches for brain tumor segmentation are built on fully convolutional neural networks ( FCNs ) using either 2D or 3D convolutions . However , 2D convolutions can not make full use of the spatial information of volumetric medical image data , while 3D convolutions suffer from high expensive computational cost and memory demand . This novel framework integrates the spatial registration task directly inside the CNN , and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy . The entire SR process relies on a single CNN with three main stages : shared 2-D convolutions to extract high-dimensional features from the input images ; a subnetwork proposing registration filters derived from the high-dimensional feature representations ; 3-D convolutions for slow fusion of the features from multiple images . The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images . Alternatively , new convolutional blocks , such as MobileNet , can be directly designed for efficiency . In this paper , we unify these two approaches by proposing a tensor factorization framework for efficient multidimensional ( separable ) convolutions of higher-order . Interestingly , the proposed framework enables a novel higher-order transduction , allowing to train a network on a given domain ( e.g. , 2D images or N-dimensional data in general ) and using transduction to generalize to higher-order data such as videos ( or (N+K)--dimensional data in general ) , capturing for instance temporal dynamics while preserving the learnt spatial information . We study the recovery of sparse vectors from subsampled random convolutions via $ \ell_1$-minimization . We consider the setup in which both the subsampling locations as well as the generating vector are chosen at random . The convolution on fine grid can be approximated by the sum of a few convolutions on coarse grid following an interpolation procedure . The CBE is verified by random matrix convolutions and intensity calculation in lithography simulation . Results show that the use of the CBE method results in similar image quality with significant running speed enhancement compared with traditional convolution method . Roto-translation equivariance for image analysis applications is the example we will be using throughout the paper . Our default PDE is solved by a combination of linear group convolutions and non-linear morphological group convolutions . 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 . In this method , we firstly apply embedding mechanism to get the semantic and syntactic of the original biomedical literature . Then a novel architecture using small convolutions is proposed , which takes raw biomedical literature as input and operates directly at the word level to get the embedding-based convolutional features . Finally , these features are fed to softmax classifier to extract DDIs from biomedical literature . However , dilated convolutions suffer from the gridding artifacts , which hampers the performance of DCNNs with dilated convolutions . In this work , we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions . Unlike existing models , which explore solutions by focusing on a block of cascaded dilated convolutional layers , our methods address the gridding artifacts by smoothing the dilated convolution itself . Convolutional transform learning is an unsupervised framework we introduced recently , for feature generation based on learnt convolutions . In this work , we propose a supervised formulation for convolutional transform so as to address the multi-label classification problem . Our implementation of density evolution covers the construction of both regular and punctured polar codes and allows for treating the construction of both code classes in a unified framework . Using our implementation , we achieve substantial reductions in the number of density convolutions necessary for the construction of punctured polar codes and obtain tight upper bounds on the block error rates . Lastly , GCNs are a recent class of algorithms based on convolutional encoders for learning node embeddings . The GCN model is motivated by a localized first - order approximation of spectral convolutions for layer - wise information propagation on graphs . Similar to our LoNGAE model , the GCN model can learn hidden layer representations that encode both local graph structure and features of nodes . PReLu non linearities are applied throughout the network . Replacing pooling operations with convolutional ones results also to networks that , depending on the specific implementation , can have a smaller memory footprint during training , due to the fact that no switches mapping the output of pooling layers back to their inputs are needed for back - propagation , and that can be better understood and analysed by applying only de - convolutions instead of un - pooling operations . Downsampling allows us to reduce the size of the signal presented as input and to increase the receptive field of the features being computed in subsequent network layers . The size of the receptive field in our network is taking RGB color channels with mean subtraction . “ reduce ” and “ reduce ” stands for the number of filters in the reduction layer used before the and convolutions . One can see the number of filters in the projection layer after the built - in max - pooling in the âpool projâ column . Proposals are generated using RPN on top of conv4 features and classification is performed after concatenating conv4 and conv5 features . In the conv5 branch , we use deformable convolutions and a stride of . We use a dimensional feature map in RPN . Socher et al . used a convolutional network in a separate stage to first learn features on RGB - Depth data , prior to hierarchical merging . In these models , the input dimension is twice that of the output and recursive convolutions are applied only two times . Similar dimension reduction occurs in the recurrent convolutional neural networks used for semantic segmentation . Both are deep convolutional networks . The first architecture is based on the Zeiler & Fergus model which consists of multiple interleaved layers of convolutions , non - linear activations , local response normalizations , and max pooling layers . We additionally add several convolution layers inspired by the work of . section : Related work Last year , BraTS 2017 , top performing submissions included Kamnitsas et al . who proposed to ensemble several models for robust segmentation ( EMMA ) , and Wang et al . who proposed to segment tumor subregions in cascade using anisotropic convolutions . EMMA takes advantage of an ensemble of several independently trained architectures . In this paper we show that an architecture based on convolutional layers is very competitive to recurrent encoders . We investigate simple average pooling as well as parameterized convolutions as an alternative to recurrent encoders and enable very deep convolutional encoders by using residual connections [ reference ][ reference ] . We experiment on several standard datasets and compare our approach to variants of recurrent encoders such as uni - directional and bi - directional LSTMs .
In this improved U-net model , the highly modular ResNeXt50 network is used as the encoder of U-net and a parallel dense residual module based on atrous convolution is proposed to extract multi-scale semantic information for segmentation . Moreover , the transposed convolution whose stride is 2 is used for upsampling to obtain the grayscale segmentation mask . This modified model adopts the sum of jaccard loss and binary cross entropy loss as the total loss function .
This paper proposes a novel deep learning-based method considering the connectivity of pixels for automatic pavement crack detection which has the potential to complement the current practice involving visual inspection which is costly , inefficient and time-consuming . In the proposed method , the convolutional layers are densely connected in a feed-forward fashion to reuse features from multiple layers , and transposed convolution layers are used for multiple level feature fusion . A novel loss function considering the connectivity of pixels is introduced to overcome the issues related to the output of transposed convolution layers .
We use the squeeze-and-excitation residual network as encoder to simultaneously extract features from RGB and depth data and fuse them in the decoder . Multiple average pooling layers and transposed convolution layers are used to process the encoded outputs and fuse their outputs over several decoder layers . To optimize the network parameters , we use a pyramid supervision training scheme , which applies supervised learning over different layers in the decoder to prevent vanishing gradients .
The acceleration architecture of transposed convolution layers is essential since transposed convolution operations , as critical components in the generative model of generative adversarial networks , are computationally intensive inherently . In addition , the pre-processing of inserting and padding with zeros for input feature maps causes many ineffective operations .
In this paper , we propose a deep neural network model for target classification in automotive radar system . In the proposed network , we introduce transposed convolutional network ( TCNet ) which applies transposed convolution operations . We discuss the properties of transposed convolution and show that TCNet can reduce the network size and improve the classification performance for the systems in which the signals are sparse and memory is restricted like our automotive radar systems .
While the FPGA-based CNN accelerators have been widely studied in prior work , the acceleration of deconvolutional networks on FPGA is rarely explored . This paper proposes a novel parametrized deconvolutional architecture based on an FPGA-friendly method , in contrast to the transposed convolution implementation in CPUs and GPUs . Hardware design templates which map this architecture to FPGAs are provided with configurable deconvolutional layer parameters .
We initialize the convolutional filters using the method of He et al . . The size of the transposed convolutional filters is and the weights are initialized from a bilinear filter . All the convolutional and transposed convolutional layers ( except the reconstruction layers ) are followed by leaky rectified linear units ( LReLUs ) with a negative slope of 0.2 .
Our model progressively predicts high - frequency residuals in a coarse - to - fine manner . By replacing the pre - defined bicubic interpolation with the learned transposed convolutional layers and optimizing the network with a robust loss function , the proposed LapSRN alleviates issues with undesired artifacts and reduces the computational complexity . Extensive evaluations on benchmark datasets demonstrate that the proposed model performs favorably against the state - of - the - art SR algorithms in terms of visual quality and run time .
Network Architecture . Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator .
For down sampling in the encoding unit , 2×2 max - pooling operations are performed . In the decoding phase , the convolution transpose ( representing up - convolution , or de - convolution ) operations are performed to up - sample the feature maps . The very first version of U - Net was used to crop and copy feature maps from the encoding unit to the decoding unit .
In the case of convolution operation , there exist doubly block circulant matrix such that . Consider which is essentially the transpose convolution of on ( ) . Thus , similar to PIM , PICO iterates between the following two steps : Update ; Do transpose convolution of on to get ; update .
[ reference ] . In addition , CV ( 128 , 256 , 4c2s ) denotes a convolutional layer with 128 input channels , 256 output channels , and kernel size 4 with stride 2 . Similarly , DC ( 256 , 128 , 4c2s ) denotes a corresponding transposed convolutional operation . Compared to the original DCGAN architecture , the discriminator under our formulation does not have the last sigmoid layer which squashes a scalar value into a probability in [ reference ][ reference ] .
Specifically , we use the standard residual block for both self - transitions . A single convolutional layer is used for the down - sampling transition and a single transposed convolutional ( or deconvolutional ) layer is used for the up - sampling transition . The strides in both inter - state layers are set to be the same as the SR upscaling factor .
Although a number of pattern recognition methods have been used to analyze the mixed gases , the performances of these methods highly depend on the hand-crafted feature engineering . By contrast , the proposed implementation , based on one-dimensional convolution , is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene , CO , and methane . To the best of our knowledge , the proposed 1D-DCNN algorithm is first applied in the mixture gases ’ recognition .
Methods We designed a CNN model and optimized it by dropout and normalization . One-dimensional convolution , max-pooling , and fully-connected multiple perceptron were used to analyze the short-term normal ECG . The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model .
The paper investigates systolic array designs for two dimensional convolution and designs for dedicated decimation filters . While existing designs for two dimensional convolution are suitable as decimation filters they can be improved by reducing the number of processing cells . A systolic array for convolution/decimation is presented which completes in time proportional to the image size with the array proportional to the window size .
We introduce a novel and generic convolutional unit , DiCE unit , that is built using dimension-wise convolutions and dimension-wise fusion . The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations ; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor . The DiCE unit is simple and can be easily plugged into any architecture to improve its efficiency and performance .
First , the energy distributions of autonomous underwater vehicle dynamic signals are given in the time-frequency plane . And then the energy concentration in the energy distribution is enhanced based on a serial signal processing , including wavelet decomposition , modified Bayes ’ classification algorithm , and two dimensional convolution operation , successively . After that the Shannon entropy of the energy distribution is calculated .
This paper presents a new formulation of digital filters that combines the description of signal-processing and arithmetic operations . This is done by noting that multiplication is a form of convolution and therefore normal one-dimensional scalar convolution is in fact two-dimensional binary convolution . This is generalized to multidimensions and can be applied with table-look-up and transform techniques .
The VSRE is being developed as a research tool for examining multi-modal ( audio-visual ) interactions through the simple manipulation of the acoustic environment and audio parameters ( sound quality ) , that will , through a series of human-based experiments , allow for the testing of the effect of varying these parameters may have on immersion , engagement , and visual fidelity perception within a virtual environment . Finally , we also provide a running time comparison of several one-dimensional convolution implementations .
For each of the two directions , the input - to - state component is simply a convolution that contributes to the four gates in the LSTM core ; the operation generates a tensor . The state - to - state recurrent component is then computed with a column - wise convolution that has a kernel of size . The step takes the previous hidden and cell states , combines the contribution of the input - to - state component and produces the next hidden and cell states , as defined in Equation [ reference ] .
For all filters , we subtract the mean value over all dimensions in each channel , in order to ensure a mean of zero in each channel . In implementing the two - dimensional convolution operation required for filtering the raw images using , we obtained only the central ‘ valid ’ region , i.e. for images of size , the total dimension of the valid region is . Consequently , the total number of features per image obtained prior to pooling , from filters , and images with channels is .
Support vector machines are used to learn from gathered information through relevance feedback . An adaptive convolution kernel is defined to handle the proposed structured multifeature space . The positive definite property of the introduced kernel is proven , as essential condition for uniqueness and optimality of the convex optimization in support vector machines .
For the problem that the segmentation of stalk candidates for randomly placed fruit cluster based on existing morphology algorithm tends to be low precision , an improved image segmentation algorithm based on adaptive morphology is proposed . According to edge distances defined based on minimum distance between edge point and unconnected components in minimum domain , the adaptive convolution kernel is constructed . In addition , a run analysis method with different and unordered labels is designed to reduce calculation time of edge distances .
In this paper , the structure of SKANet is constructed similarly to that of ResNeXt by repeating residual blocks with the same topology and group convolution for saving computational costs . Different from ResNeXt , the primary convolution is replaced by using selective kernel convolution to adaptively adjust the receptive field based on imported information . Then , attention mechanism is added to the bottom of the block to emphasize on important features and suppress unnecessary ones for more accurate representation of the network .
As to the phenomenon that traditional convolutional neural network using a single channel and a single scale convolution kernel to extract image feature leading to low classification accuracy , the cascade convolution module used to extract image feature in this paper . The module uses three different sets of convolution kernels to extract feature .
The algorithm has been shown effective when each channel is prime . In this paper , we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism , according to the uncertainty of the convolution kernel size in MBD processing . To reduce the interference of noise , we amend the convolution kernel in an iterative process , and finally restore a clear image .
This feature has a greater degree of division in frequency dimension with other target echoes . Two dimensional convolution filtering of frequency domain adaptive convolution kernel and measured echo data is carried out . The filtering results can be used for echo identification of helicopter main rotor .
In this paper , the recognition method of no-seedlings grids of tray based on deep convolutional neural network ( GoogLeNet model ) is proposed , which uses 9 Inception-V3 modules to construct deep convolutional neural network . Inception-V3 modules use selective multi-scale convolution kernels to extract grid seedlings features , use the convolution kernels factorization to reduce the operation parameters and use Batch Normalization algorithm to improve the model training speed to build the predictive recognition model of tray no-seedlings grids . To verify the effectiveness of this method , this research designed an images acquisition device , collected 3400 pepper tray grids image samples , label the samples by manual .
These improvements , which we demonstrate numerically using a lipid membrane system , arise because the u-series is smooth on the entire real axis and exact up to the cutoff radius . Additional performance improvements over the Ewald decomposition may be possible in certain situations because the far part of the u-series is a sum of Gaussians and can thus be evaluated using algorithms that require a separable convolution kernel ; we describe one such algorithm that reduces communication latency at the expense of communication bandwidth and computation , a trade-off that may be advantageous on modern massively parallel supercomputers .
Separable kernel convolution is a fundamental operation for image processing and computer vision applications . Existing parallel implementations of convolution operation convolve a whole image with a separable kernel .
In this work , we introduce a novel approach for achieving a lightweight model from the views of reconstructing the structure of convolutional kernels and efficient storage . Our approach transforms a traditional square convolution kernel to line segments , and automatically learn a proper strategy for equipping these line segments to model diverse features . The experimental results indicate that our approach can massively reduce the number of parameters ( pruned 69 % on DenseNet-40 ) and calculations ( pruned 59 % on DenseNet-40 ) while maintaining acceptable performance ( only lose less than 2 % accuracy ) .
The output signal error is modelled with the help of a spatio-statistical parameter ( sampling phase ) that depends on the resolution of the input data grid . A frequency domain algorithm is then developed which computes the decay rate of a certain output error functional with respect to the data resolution level , using the power spectra of the input signal , the chosen scaling estimation kernel , and the theoretic convolution kernel of the linear system . A simple numerical experiment is also included to compare the accuracy of the classic FFT approach in SISO approximation problems against the proposed generalization that utilizes an arbitrary reference scaling kernel .
Feature maps from different convolution kernels are concatenated as current layer ’s output . To decrease the correlations among different convolution kernels , the dilated convolution is used rather than general convolution kernels . Through this way , multi - scale context knowledge is obtained at the same layer .
Then we use four multi - scale convolution layers to obtain the complex image context information . In each multi - scale convolution layer , we use a convolution kernel with size . To obtain multi - scale receptive fields , we adopt dilated convolution for the convolution filters .
We assume that ’s are sorted ascendingly and arrange ’s accordingly . The multiplers are the spectral representation of convolution kernel . Denote the set of multipliers as , each corresponds to one .
Since many natural functions we care about could be depicted by a small number of low - frequency laplacian eigenbases , we compute and use the smallest eigenvalues as well as the corresponding eigenbases for each in all our experiments . The choice of dilation parameters , number of output channels after each convolution layer , number of learnable parameters in each convolution kernel are shown in Table [ reference ] . We choose in all of our experiments .
Non - linearities allow the networks to exploit the full input field , or to focus on fewer elements if needed . section : Each convolution kernel is parameterized as and takes as input X ∈ R k×d which is a concatenation of k input elements embedded in d dimensions and maps them to a single output element Y ∈ R 2d that has twice the dimensionality of the input elements ; subsequent layers operate over the k output elements of the previous layer .
Computing a feature map in a convolutional layer ( see Figure [ reference ] ) consists of the following three steps : Convolution of kernels ( filters ) : Each feature map is associated with one kernel ( or several , in the case of Maxout ) .
A ReLU activation function is applied after all convolution unless otherwise noted . Once we have the interaction tensor , we use a convolution with kernel to scale down the tensor in a ratio , , without following ReLU . If the input channel is then the output channel is .
The proposed novel training scheme is not only computationally efficient but also offers an adaptive way of partially alleviating the inherent class - imbalance of segmentation problems . We analyzed the benefits of using small convolutional kernels in 3D CNNs , which allowed us to develop a deeper and thus more discriminative network , without increasing the computational cost and number of trainable parameters . We discussed the challenges of training deep neural networks and the adopted solutions from the latest advances in deep learning .
Since manual segmentation is time-consuming , tedious and error-prone , a fully automatic method for brain tumor segmentation is needed . Recently , state-of-the-art approaches for brain tumor segmentation are built on fully convolutional neural networks ( FCNs ) using either 2D or 3D convolutions . However , 2D convolutions can not make full use of the spatial information of volumetric medical image data , while 3D convolutions suffer from high expensive computational cost and memory demand .
This novel framework integrates the spatial registration task directly inside the CNN , and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy . The entire SR process relies on a single CNN with three main stages : shared 2-D convolutions to extract high-dimensional features from the input images ; a subnetwork proposing registration filters derived from the high-dimensional feature representations ; 3-D convolutions for slow fusion of the features from multiple images . The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images .
Alternatively , new convolutional blocks , such as MobileNet , can be directly designed for efficiency . In this paper , we unify these two approaches by proposing a tensor factorization framework for efficient multidimensional ( separable ) convolutions of higher-order . Interestingly , the proposed framework enables a novel higher-order transduction , allowing to train a network on a given domain ( e.g. , 2D images or N-dimensional data in general ) and using transduction to generalize to higher-order data such as videos ( or (N+K)--dimensional data in general ) , capturing for instance temporal dynamics while preserving the learnt spatial information .
We study the recovery of sparse vectors from subsampled random convolutions via $ \ell_1$-minimization . We consider the setup in which both the subsampling locations as well as the generating vector are chosen at random .
The convolution on fine grid can be approximated by the sum of a few convolutions on coarse grid following an interpolation procedure . The CBE is verified by random matrix convolutions and intensity calculation in lithography simulation . Results show that the use of the CBE method results in similar image quality with significant running speed enhancement compared with traditional convolution method .
Roto-translation equivariance for image analysis applications is the example we will be using throughout the paper . Our default PDE is solved by a combination of linear group convolutions and non-linear morphological group convolutions . 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 .
In this method , we firstly apply embedding mechanism to get the semantic and syntactic of the original biomedical literature . Then a novel architecture using small convolutions is proposed , which takes raw biomedical literature as input and operates directly at the word level to get the embedding-based convolutional features . Finally , these features are fed to softmax classifier to extract DDIs from biomedical literature .
However , dilated convolutions suffer from the gridding artifacts , which hampers the performance of DCNNs with dilated convolutions . In this work , we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions . Unlike existing models , which explore solutions by focusing on a block of cascaded dilated convolutional layers , our methods address the gridding artifacts by smoothing the dilated convolution itself .
Convolutional transform learning is an unsupervised framework we introduced recently , for feature generation based on learnt convolutions . In this work , we propose a supervised formulation for convolutional transform so as to address the multi-label classification problem .
Our implementation of density evolution covers the construction of both regular and punctured polar codes and allows for treating the construction of both code classes in a unified framework . Using our implementation , we achieve substantial reductions in the number of density convolutions necessary for the construction of punctured polar codes and obtain tight upper bounds on the block error rates .
Lastly , GCNs are a recent class of algorithms based on convolutional encoders for learning node embeddings . The GCN model is motivated by a localized first - order approximation of spectral convolutions for layer - wise information propagation on graphs . Similar to our LoNGAE model , the GCN model can learn hidden layer representations that encode both local graph structure and features of nodes .
PReLu non linearities are applied throughout the network . Replacing pooling operations with convolutional ones results also to networks that , depending on the specific implementation , can have a smaller memory footprint during training , due to the fact that no switches mapping the output of pooling layers back to their inputs are needed for back - propagation , and that can be better understood and analysed by applying only de - convolutions instead of un - pooling operations . Downsampling allows us to reduce the size of the signal presented as input and to increase the receptive field of the features being computed in subsequent network layers .
The size of the receptive field in our network is taking RGB color channels with mean subtraction . “ reduce ” and “ reduce ” stands for the number of filters in the reduction layer used before the and convolutions . One can see the number of filters in the projection layer after the built - in max - pooling in the âpool projâ column .
Proposals are generated using RPN on top of conv4 features and classification is performed after concatenating conv4 and conv5 features . In the conv5 branch , we use deformable convolutions and a stride of . We use a dimensional feature map in RPN .
Socher et al . used a convolutional network in a separate stage to first learn features on RGB - Depth data , prior to hierarchical merging . In these models , the input dimension is twice that of the output and recursive convolutions are applied only two times . Similar dimension reduction occurs in the recurrent convolutional neural networks used for semantic segmentation .
Both are deep convolutional networks . The first architecture is based on the Zeiler & Fergus model which consists of multiple interleaved layers of convolutions , non - linear activations , local response normalizations , and max pooling layers . We additionally add several convolution layers inspired by the work of .
section : Related work Last year , BraTS 2017 , top performing submissions included Kamnitsas et al . who proposed to ensemble several models for robust segmentation ( EMMA ) , and Wang et al . who proposed to segment tumor subregions in cascade using anisotropic convolutions . EMMA takes advantage of an ensemble of several independently trained architectures .
In this paper we show that an architecture based on convolutional layers is very competitive to recurrent encoders . We investigate simple average pooling as well as parameterized convolutions as an alternative to recurrent encoders and enable very deep convolutional encoders by using residual connections [ reference ][ reference ] . We experiment on several standard datasets and compare our approach to variants of recurrent encoders such as uni - directional and bi - directional LSTMs .
Semantic Segmentation of Buildings in Remote Sensing Images Based on Dense Residual Learning and Channel Adaption Densely connected deep neural network considering connectivity of pixels for automatic crack detection DMFNet: Deep Multi-Modal Fusion Network for RGB-D Indoor Scene Segmentation Exploring Efficient Acceleration Architecture for Winograd-Transformed Transposed Convolution of GANs on FPGAs Moving Target Classification In Automotive Radar Systems Using Transposed Convolutional Networks Memory-Efficient Architecture for Accelerating Generative Networks on FPGA Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation Improving MMD-GAN Training with Repulsive Loss Function Calibrating Energy-based Generative Adversarial Networks Image Super-Resolution via Dual-State Recurrent Networks Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network Automatic Prediction of Atrial Fibrillation Based on Convolutional Neural Network Using a Short-term Normal Electrocardiogram Signal Systolic arrays for 2D decimation filters DiCENet: Dimension-wise Convolutions for Efficient Networks A fault diagnosis approach for autonomous underwater vehicle thrusters using time-frequency entropy enhancement and boundary constraint–assisted relative gray relational grade Digital filter structures described by distributed arithmetic Spatial sound rendering for dynamic virtual environments Pixel Recurrent Neural Networks Enhanced image classification with a fast-learning shallow convolutional neural network An Object- and User-Driven System for Semantic-Based Image Annotation and Retrieval Grasping Point Detection of Randomly Placed Fruit Cluster Using Adaptive Morphology Segmentation and Principal Component Classification of Multiple Features Early Diagnosis of Alzheimer's Disease Based on Selective Kernel Network with Spatial Attention Design of Convolutional Network Based on Cascade Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection Helicopter Target Recognition Based on the Frequency Domain Adaptive Convolution Kernel Filtering Recognition Method of No-seedling Grids of Trays based on Deep Convolutional Neural Network The u-series: A separable decomposition for electrostatics computation with improved accuracy. Efficient parallel selective separable-kernel convolution on heterogeneous processors Learning Efficient Convolutional Networks through Irregular Convolutional Kernels Aliasing Error Modelling in Single-Input Single-Output Linear Estimation Systems Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Convolutional Sequence to Sequence Learning Brain tumor segmentation with Deep Neural Networks Natural Language Inference over Interaction Space Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation S3D-UNet: Separable 3D U-Net for Brain Tumor Segmentation DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images Factorized Higher-Order CNNs With an Application to Spatio-Temporal Emotion Estimation Improved bounds for sparse recovery from subsampled random convolutions Fast Convolution Method and Its Application in Mask Optimization for Intensity Calculation Using Basis Expansion PDE-based Group Equivariant Convolutional Neural Networks Deep Convolution Neural Networks for Drug-Drug Interaction Extraction Smoothed Dilated Convolutions for Improved Dense Prediction Multi-Label Consistent Convolutional Transform Learning: Application to Non-Intrusive Load Monitoring Efficient Implementation of Density Evolution for Punctured Polar Codes Learning to Make Predictions on Graphs with Autoencoders V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Going deeper with convolutions SNIPER: Efficient Multi-Scale Training Deeply-Recursive Convolutional Network for Image Super-Resolution FaceNet: A unified embedding for face recognition and clustering 3D MRI brain tumor segmentation using autoencoder regularization A Convolutional Encoder Model for Neural Machine Translation
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2019 2020 2019 2020 2018 2018 2017 2018 2018 2019 2017 2018 2019 2019 1996 2019 2020 1977 2013 2016 2015 2007 2019 2019 2019 2016 2018 2019 2020 2010 2019 2001 2017 2017 2017 2017 2018 2017 2018 2020 2019 2014 2020 2018 2018 2020 2019 2018 2016 2015 2018 2016 2015 2018 2017
ACSSC FPT 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ICLR ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition DSP ICML 2015 International Joint Conference on Neural Networks (IJCNN) ACPR (2) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICML Medical Image Anal. ICLR Medical Image Anal. BrainLes@MICCAI (2) BIBM KDD ICASSP 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) 2016 Fourth International Conference on 3D Vision (3DV) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) BrainLes@MICCAI ACL
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