This new model consists of a multi-scale atrous convolution module and two bottleneck residual units , which greatly increase the width and depth of the network . In addition , we use depthwise separable convolution instead of traditional 2D or 3D convolution to extract spatial and spectral features . Furthermore , considering classification accuracy can benifit from multi-scale information , we introduce atrous convolution with different dilation rates parallelly to extract more discriminant features of HSIs for classification . In order to discriminate the real targets , the clutter and the dense multi-false targets , we propose a factorized convolutional neural network-based algorithm for radar targets discrimination . We establish the factorized convolutional neural network model with depthwise separable convolution . To reduce the parameters of the model , we establish the simplified factorized convolutional neural network by reducing the numbers of both convolutional filters and connection nodes of fully connected layers . Aiming at the shortcoming of deep neural network in crop disease diagnosis , a lightweight compressed depth neural network for tomato disease diagnosis is proposed . Multi-scale convolution is used to increase receptive field , extract more abundant features , reduce model parameters and realize lightweight of the network by adopting the strategies of group convolution , depth separable convolution , pointwise convolution , channel shuffle , etc . For the lightweight model that has been initially trained , pruning operation is used to cut filter weight that is not important to reduce redundancy of the model . Instance segmentation algorithms usually require accurate pixel-level supervised labels , which are difficult to collect , so we propose a weakly supervised instance segmentation assignment to solve this problem . The lightweight model uses multi-scale convolution to expand the network width , which makes the extracted features richer , and depth separable convolution is adopted to reduce model parameters . The experimental results showed that our method reached higher recognition accuracy when compared with other methods , at the same time occupied less memory space , which can realize the real-time recognition of tomato diseases on low-performance terminals , and can be applied to the recognition of crop diseases in other similar application scenarios . The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples . A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data . We trained the two classifiers for each label using the improved DenseNet to classify different labels . APPROACH In this work we introduce EEGNet , a compact convolutional neural network for EEG-based BCIs . We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI . We compare EEGNet , both for within-subject and cross-subject classification , to current state-of-the-art approaches across four BCI paradigms : P300 visual-evoked potentials , error-related negativity responses ( ERN ) , movement-related cortical potentials ( MRCP ) , and sensory motor rhythms ( SMR ) . Separable convolutions xception2016 , however , decrease the complexity considerably , to . Even with , however , the complexity of a separable convolution is equal to the combination of a self - attention layer and a point - wise feed - forward layer , the approach we take in our model . As side benefit , self - attention could yield more interpretable models . Recently , convolutional sequence - to - sequence networks have been applied to machine translation tasks with good results . In this work , we study how depthwise separable convolutions can be applied to neural machine translation . We introduce a new architecture inspired by Xception and ByteNet , called SliceNet , which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet , and , with a similar parameter count , achieves new state - of - the - art results . with the aim of reducing its number of parameters , storage size and latency with minimal performance degradation . To achieve these improvements we used Temporal Depthwise Separable Convolution and Global Average Pooling techniques . Therefore , our main contribution is to propose the Squeezed Very Deep Convolutional Neural Networks ( SVDCNN ) , a text classification model which requires significantly fewer parameters compared to the state - of - the - art CNNs . Many highly accurate classification models based on CNN have been developed for various machine learning applications , but they generally computationally costly and require a hardware-based platform with super computing power and memory resources to implement the algorithm . In order to accurately and efficiently achieve object detection tasks using CNN on a system with limited resources such as a mobile device , we propose an innovative type of DenseNet , which is a lightweight convolutional neural network algorithm called Lite Asymmetric DenseNet ( LADenseNet ) . Aiming to compress the CNN model complexity , we replace the 7 x 7 convolution and 3 x 3 max-pool with multiple 3 x 3 convolutions and a 2 x 2 max-pool in the initial down-sampling process to significantly reduce the computing cost . A Lightweight Convolutional Neural Network . The advancement of Industry 4.0 and Industrial Internet of Things ( IIoT ) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis . The experimental results have demonstrated that the classification accuracy can achieve 97.39 % , which is 3.81 % higher than that obtained from the conventional methods . In addition , for this vehicle classification task , the proposed lightweight convolutional network can achieve comparable or even higher performance compared to the deep convolutional neural networks , while the proposed method does not need the support of a graphics processing unit and has much lower complexity without the training process . Our two-stage approach utilizes both voxel representation and raw point cloud data to exploit respective advantages . The first stage network , with voxel representation as input , only consists of light convolutional operations , producing a small number of high-quality initial predictions . Coordinate and indexed convolutional feature of each point in initial prediction are effectively fused with the attention mechanism , preserving both accurate localization and context information . This significantly slows down the tracking speed and increases the risk of over-fitting . In this work , we tackle the problems of model complexity and over-fitting by introducing Lightweight Convolution Operators ( LCO ) . Our LCO tracker performs dimensionality reduction and spatial constraints on the correlation filters to reduce the model complexity and accelerate the tracking speed . In this work , we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences . Such representations are useful for building NLP systems , including conversational agents . At the same time , the network structure of this method is complex , and the detection accuracy and speed can not be well balanced . This paper proposes a new method for detecting railway surface defects based on end-to-end target detection and lightweight convolutional network structure . This method uses MobileNetV2 as the backbone network to extract image features , and then performs multi-scale defect detection to ensure real-time . In this paper , we propose an effective retraining method for optimizing the Convolutional Neural Network ( CNN ) features towards Content Based Image Retrieval ( CBIR ) . To accomplish this goal , we utilize a pretrained CNN model , and we adapt it to a lightweight fully convolutional model which allows for producing compact image representations , reducing the storage requirements . Subsequently , we obtain the feature representations of the last convolutional layer using the max pooling operation , and we retrain the weights of the convolutional layers in a fully unsupervised fashion , aiming to produce more efficient compact image descriptors which improve the retrieval performance both in terms of time and precision . Hence , our contribution is two-fold . First , we introduce a model which combines a lightweight fully convolutional architecture with conditional random field postprocessing to solve the task of printed and handwritten text recognition on a pixel-level , and secondly , we describe how our model is deployed as a web service . Thereafter , all layers in our network are convolutional layers . Our network is fully - convolutional . subsection : Combining Segments with Links One can simply replace deconvolution with convolution , which results in an architecture that is very similar to recently proposed very deep fully convolutional neural networks . However , there exist essential differences between a fully convolution model and our model . Take image denoising as an example . One can simply replace deconvolution with convolution , which results in a architecture that is very similar to recently proposed very deep fully convolutional neural networks . However , there exist essential differences between a fully convolution model and our model . In the fully convolution case , the noise is eliminated step by step , i.e. , the noise level is reduced after each layer . Ning et al . define a convnet for coarse multiclass segmentation of C. elegans tissues with fully convolutional inference . Fully convolutional computation has also been exploited in the present era of many - layered nets . Sliding window detection by Sermanet et al . , semantic segmentation by Pinheiro and Collobert , and image restoration by Eigen et al . do fully convolutional inference . New formulations are given which involve two-variable polynomials using the CRT decomposition on both of the two dimensions for the realization of the FPT . This approach substantially reduces the number of operations for the realization of two-dimensional convolutions , especially in terms of the numbers of multiplications.<<ETX > > The system architecture and the algorithms for the two applications , viz . FFT and two-dimensional convolutions , are discussed . A method for finding curves in digital images with speckle noise is described . The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features . Maximum a posteriori ( MAP ) estimation is used , together with statistical models for the speckle noise and for the curve-generation process , to find the most probable estimate of the feature , given the image data . As we show in our experiments , the size of the receptive field is directly related to the performance of dense tasks such as semantic segmentation . We look at different network architectures and mechanisms to increase the receptive field size of point convolutions and propose in particular dilated point convolutions . Importantly , our dilation mechanism can easily be integrated into all existing methods using nearest-neighbor-based point convolutions . For this study , a distance to agreement was set to 3 mm while the dose difference varied from 2 % to 10 % . In general all algorithms based on pencil-beam convolutions showed a systematic deficiency in managing the presence of heterogeneous media . In contrast , complicated patterns were observed for the advanced algorithms with significant discrepancies observed between algorithms in the lighter materials ( rho = 0.035 g cm(-3 ) ) , enhanced for the most energetic beam . In this paper , we examine and recommend critical architectural changes that forge an optimal multi-scale feature extraction process . To this end , we replace regular $ 1-$D convolutions with adaptive dilated convolutions that have innate capability of capturing increased context by using large temporal receptive fields . We also investigate the impact of dense connections on the extraction process that encourage feature reuse and better gradient flow . Efficient implementation of low-level image processing depends heavily on separability of the convolution kernels that form the fundamental building blocks of most algorithms . Due to the curvature of the sphere , it is not possible to place an orthogonal grid pixelation globally on its surface , making direct application of classical separable kernel convolutions impossible . In the spherepix data structure , we propose an alternative approach consisting of a collection of overlapping ( near orthogonal ) grid patches covering the sphere 's surface . To solve the said problems , the model of convolutional neural network in the deep learning approach was used in this article to cope with classification and labeling tasks of brain tumor images . The main contents of this article were studied as follows : the principle and operating approach of convolutional neural network on image processing was first introduced , and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures ; considering the inter-label dependency in pixel areas , the situation of conditional random field was simulated to design the input series connection structure ; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited ; finally , the classification accuracy upon experiments reached 83 % , which has verified the effectiveness of model to improve . An N point DCT can be computed through 2 pair of [ (M-1)/2 ] point cyclic convolutions where M is an odd number such that N=2 M . The proposed architecture uses only a pair of systolic array where inputs are pipelined against the one in literature where 2 pairs of systolic arrays are used . The architecture provides a way to organize the computation in order to lower the I/O bandwidth to 2 pixels per clock cycle , without increasing the internal storage . As a result , the whole silicon array can be dedicated to computation , without excessive external memory requirements , opening the way to single-chip , very-large-kernel convolutions . As an example , a 16 * 16 convolution or correlation architecture has been devised based on a 1.2- mu m CMOS process . 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 . The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions As can be seen above , the size ( and cost ) of a separable convolution with channels and a receptive field of size is . The combined vector is then passed through a non - linear layer to produce the hidden representation : The deep convolutional network ( Figure [ reference ] b ) adds an extra convolutional layer to the architecture , using the first layer as input . It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset . In a recurrent neural network ( RNN ) , each hidden representation is calculated based on the current token embedding and the hidden vector at the previous time step : where is a nonlinear function , such as the sigmoid function . Our best performance on a development set ( TED.tst2013 ) was achieved using a four - layer encoder – decoder QRNN with 320 units per layer , no dropout or regularization , and gradient rescaling to a maximum magnitude of 5 . Inputs were supplied to the encoder reversed , while the encoder convolutions were not masked . The first encoder layer used convolutional filter width , while the other encoder layers used . Our experiments in § 6 confirm this : using as few as k = 4 eigenvectors per channel for patches of size m = 5 is sufficient . As our second speedup , we observe that the projection Qᵀp can be computed by a series of k convolutions between a channel image and each m×m filter reshaped from its corresponding eigenvector ( column of Q ) . This is possible because the covariance matrix Σ is shared across all patches per channel and hence the derived Q is likewise spatially invariant . subsection : Submanifold Convolutional Networks We use a combination of VSC convolutions , strided SC convolutions , and sparse pooling operations to build sparse versions of the popular VGG , ResNet , and DenseNet convolutional networks . The blocks we use in our networks are presented in Figure [ reference ] . The core idea is to equip each original convolutional layer with another low-cost collaborative layer ( LCCL ) , and the element-wise multiplication of the ReLU outputs of these two parallel layers produces the layer-wise output . The combined layer is potentially more discriminative than the original convolutional layer , and its inference is faster for two reasons : 1 ) the zero cells of the LCCL feature maps will remain zero after element-wise multiplication , and thus it is safe to skip the calculation of the corresponding high-cost convolution in the original convolutional layer , 2 ) LCCL is very fast if it is implemented as a 1 * 1 convolution or only a single filter shared by all channels . Extensive experiments on the CIFAR-10 , CIFAR-100 and ILSCRC-2012 benchmarks show that our proposed network structure can accelerate the inference process by 32 % on average with negligible performance drop . In addition , the stacked local residual block ( LRB ) structure is used for non-linear mapping , which can effectively overcome the problem of image degradation . After extracting features , use 1 × 1 convolution to perform dimensional compression , and expand the dimensions after non-linear mapping , which can reduce the calculation amount of the model . In the reconstruction layer , deconvolution is used to enlarge the image to the required size . In this letter , we proposed a novel method to extract convective clouds . We introduce a novel deep network using only $ 1 \times 1 $ convolution ( 3ONet ) to extract the spectral characteristics . We then combine a 3ONet with the symmetrical dense-shortcut deep fully convolutional networks ( SDFCNs ) with a double-stream fully convolutional network to extract convective clouds . Unlike existing RGBT trackers , our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation , low illumination , background clutter and occlusion . In particular , we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution , and use 1x1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation . To model the interactions between RGB and thermal modalities , we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources . The cropping is necessary due to the loss of border pixels in every convolution . At the final layer a 1x1 convolution is used to map each 64 - component feature vector to the desired number of classes . In total the network has 23 convolutional layers . Using a 2k factorial experiment design , we evaluated the impact of several aspects such as database size , types of models and training parameters in the quality of automatic translation obtained . The results of the experiments were very promising and point to an initial superiority of the LightConv model in most of the evaluated scenarios .
This new model consists of a multi-scale atrous convolution module and two bottleneck residual units , which greatly increase the width and depth of the network . In addition , we use depthwise separable convolution instead of traditional 2D or 3D convolution to extract spatial and spectral features . Furthermore , considering classification accuracy can benifit from multi-scale information , we introduce atrous convolution with different dilation rates parallelly to extract more discriminant features of HSIs for classification .
In order to discriminate the real targets , the clutter and the dense multi-false targets , we propose a factorized convolutional neural network-based algorithm for radar targets discrimination . We establish the factorized convolutional neural network model with depthwise separable convolution . To reduce the parameters of the model , we establish the simplified factorized convolutional neural network by reducing the numbers of both convolutional filters and connection nodes of fully connected layers .
Aiming at the shortcoming of deep neural network in crop disease diagnosis , a lightweight compressed depth neural network for tomato disease diagnosis is proposed . Multi-scale convolution is used to increase receptive field , extract more abundant features , reduce model parameters and realize lightweight of the network by adopting the strategies of group convolution , depth separable convolution , pointwise convolution , channel shuffle , etc . For the lightweight model that has been initially trained , pruning operation is used to cut filter weight that is not important to reduce redundancy of the model .
Instance segmentation algorithms usually require accurate pixel-level supervised labels , which are difficult to collect , so we propose a weakly supervised instance segmentation assignment to solve this problem . The lightweight model uses multi-scale convolution to expand the network width , which makes the extracted features richer , and depth separable convolution is adopted to reduce model parameters . The experimental results showed that our method reached higher recognition accuracy when compared with other methods , at the same time occupied less memory space , which can realize the real-time recognition of tomato diseases on low-performance terminals , and can be applied to the recognition of crop diseases in other similar application scenarios .
The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples . A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data . We trained the two classifiers for each label using the improved DenseNet to classify different labels .
APPROACH In this work we introduce EEGNet , a compact convolutional neural network for EEG-based BCIs . We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI . We compare EEGNet , both for within-subject and cross-subject classification , to current state-of-the-art approaches across four BCI paradigms : P300 visual-evoked potentials , error-related negativity responses ( ERN ) , movement-related cortical potentials ( MRCP ) , and sensory motor rhythms ( SMR ) .
Separable convolutions xception2016 , however , decrease the complexity considerably , to . Even with , however , the complexity of a separable convolution is equal to the combination of a self - attention layer and a point - wise feed - forward layer , the approach we take in our model . As side benefit , self - attention could yield more interpretable models .
Recently , convolutional sequence - to - sequence networks have been applied to machine translation tasks with good results . In this work , we study how depthwise separable convolutions can be applied to neural machine translation . We introduce a new architecture inspired by Xception and ByteNet , called SliceNet , which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet , and , with a similar parameter count , achieves new state - of - the - art results .
with the aim of reducing its number of parameters , storage size and latency with minimal performance degradation . To achieve these improvements we used Temporal Depthwise Separable Convolution and Global Average Pooling techniques . Therefore , our main contribution is to propose the Squeezed Very Deep Convolutional Neural Networks ( SVDCNN ) , a text classification model which requires significantly fewer parameters compared to the state - of - the - art CNNs .
Many highly accurate classification models based on CNN have been developed for various machine learning applications , but they generally computationally costly and require a hardware-based platform with super computing power and memory resources to implement the algorithm . In order to accurately and efficiently achieve object detection tasks using CNN on a system with limited resources such as a mobile device , we propose an innovative type of DenseNet , which is a lightweight convolutional neural network algorithm called Lite Asymmetric DenseNet ( LADenseNet ) . Aiming to compress the CNN model complexity , we replace the 7 x 7 convolution and 3 x 3 max-pool with multiple 3 x 3 convolutions and a 2 x 2 max-pool in the initial down-sampling process to significantly reduce the computing cost .
A Lightweight Convolutional Neural Network . The advancement of Industry 4.0 and Industrial Internet of Things ( IIoT ) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis .
The experimental results have demonstrated that the classification accuracy can achieve 97.39 % , which is 3.81 % higher than that obtained from the conventional methods . In addition , for this vehicle classification task , the proposed lightweight convolutional network can achieve comparable or even higher performance compared to the deep convolutional neural networks , while the proposed method does not need the support of a graphics processing unit and has much lower complexity without the training process .
Our two-stage approach utilizes both voxel representation and raw point cloud data to exploit respective advantages . The first stage network , with voxel representation as input , only consists of light convolutional operations , producing a small number of high-quality initial predictions . Coordinate and indexed convolutional feature of each point in initial prediction are effectively fused with the attention mechanism , preserving both accurate localization and context information .
This significantly slows down the tracking speed and increases the risk of over-fitting . In this work , we tackle the problems of model complexity and over-fitting by introducing Lightweight Convolution Operators ( LCO ) . Our LCO tracker performs dimensionality reduction and spatial constraints on the correlation filters to reduce the model complexity and accelerate the tracking speed .
In this work , we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences . Such representations are useful for building NLP systems , including conversational agents .
At the same time , the network structure of this method is complex , and the detection accuracy and speed can not be well balanced . This paper proposes a new method for detecting railway surface defects based on end-to-end target detection and lightweight convolutional network structure . This method uses MobileNetV2 as the backbone network to extract image features , and then performs multi-scale defect detection to ensure real-time .
In this paper , we propose an effective retraining method for optimizing the Convolutional Neural Network ( CNN ) features towards Content Based Image Retrieval ( CBIR ) . To accomplish this goal , we utilize a pretrained CNN model , and we adapt it to a lightweight fully convolutional model which allows for producing compact image representations , reducing the storage requirements . Subsequently , we obtain the feature representations of the last convolutional layer using the max pooling operation , and we retrain the weights of the convolutional layers in a fully unsupervised fashion , aiming to produce more efficient compact image descriptors which improve the retrieval performance both in terms of time and precision .
Hence , our contribution is two-fold . First , we introduce a model which combines a lightweight fully convolutional architecture with conditional random field postprocessing to solve the task of printed and handwritten text recognition on a pixel-level , and secondly , we describe how our model is deployed as a web service .
Thereafter , all layers in our network are convolutional layers . Our network is fully - convolutional . subsection : Combining Segments with Links
One can simply replace deconvolution with convolution , which results in an architecture that is very similar to recently proposed very deep fully convolutional neural networks . However , there exist essential differences between a fully convolution model and our model . Take image denoising as an example .
One can simply replace deconvolution with convolution , which results in a architecture that is very similar to recently proposed very deep fully convolutional neural networks . However , there exist essential differences between a fully convolution model and our model . In the fully convolution case , the noise is eliminated step by step , i.e. , the noise level is reduced after each layer .
Ning et al . define a convnet for coarse multiclass segmentation of C. elegans tissues with fully convolutional inference . Fully convolutional computation has also been exploited in the present era of many - layered nets . Sliding window detection by Sermanet et al . , semantic segmentation by Pinheiro and Collobert , and image restoration by Eigen et al . do fully convolutional inference .
New formulations are given which involve two-variable polynomials using the CRT decomposition on both of the two dimensions for the realization of the FPT . This approach substantially reduces the number of operations for the realization of two-dimensional convolutions , especially in terms of the numbers of multiplications.<<ETX > >
The system architecture and the algorithms for the two applications , viz . FFT and two-dimensional convolutions , are discussed .
A method for finding curves in digital images with speckle noise is described . The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features . Maximum a posteriori ( MAP ) estimation is used , together with statistical models for the speckle noise and for the curve-generation process , to find the most probable estimate of the feature , given the image data .
As we show in our experiments , the size of the receptive field is directly related to the performance of dense tasks such as semantic segmentation . We look at different network architectures and mechanisms to increase the receptive field size of point convolutions and propose in particular dilated point convolutions . Importantly , our dilation mechanism can easily be integrated into all existing methods using nearest-neighbor-based point convolutions .
For this study , a distance to agreement was set to 3 mm while the dose difference varied from 2 % to 10 % . In general all algorithms based on pencil-beam convolutions showed a systematic deficiency in managing the presence of heterogeneous media . In contrast , complicated patterns were observed for the advanced algorithms with significant discrepancies observed between algorithms in the lighter materials ( rho = 0.035 g cm(-3 ) ) , enhanced for the most energetic beam .
In this paper , we examine and recommend critical architectural changes that forge an optimal multi-scale feature extraction process . To this end , we replace regular $ 1-$D convolutions with adaptive dilated convolutions that have innate capability of capturing increased context by using large temporal receptive fields . We also investigate the impact of dense connections on the extraction process that encourage feature reuse and better gradient flow .
Efficient implementation of low-level image processing depends heavily on separability of the convolution kernels that form the fundamental building blocks of most algorithms . Due to the curvature of the sphere , it is not possible to place an orthogonal grid pixelation globally on its surface , making direct application of classical separable kernel convolutions impossible . In the spherepix data structure , we propose an alternative approach consisting of a collection of overlapping ( near orthogonal ) grid patches covering the sphere 's surface .
To solve the said problems , the model of convolutional neural network in the deep learning approach was used in this article to cope with classification and labeling tasks of brain tumor images . The main contents of this article were studied as follows : the principle and operating approach of convolutional neural network on image processing was first introduced , and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures ; considering the inter-label dependency in pixel areas , the situation of conditional random field was simulated to design the input series connection structure ; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited ; finally , the classification accuracy upon experiments reached 83 % , which has verified the effectiveness of model to improve .
An N point DCT can be computed through 2 pair of [ (M-1)/2 ] point cyclic convolutions where M is an odd number such that N=2 M . The proposed architecture uses only a pair of systolic array where inputs are pipelined against the one in literature where 2 pairs of systolic arrays are used .
The architecture provides a way to organize the computation in order to lower the I/O bandwidth to 2 pixels per clock cycle , without increasing the internal storage . As a result , the whole silicon array can be dedicated to computation , without excessive external memory requirements , opening the way to single-chip , very-large-kernel convolutions . As an example , a 16 * 16 convolution or correlation architecture has been devised based on a 1.2- mu m CMOS process .
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 .
The number of parameters for a regular convolution , separable convolution , and group convolution with groups is : subsection : Super - separable convolutions As can be seen above , the size ( and cost ) of a separable convolution with channels and a receptive field of size is .
The combined vector is then passed through a non - linear layer to produce the hidden representation : The deep convolutional network ( Figure [ reference ] b ) adds an extra convolutional layer to the architecture , using the first layer as input . It creates convolutions of convolutions , thereby capturing more complex higher - order features from the dataset . In a recurrent neural network ( RNN ) , each hidden representation is calculated based on the current token embedding and the hidden vector at the previous time step : where is a nonlinear function , such as the sigmoid function .
Our best performance on a development set ( TED.tst2013 ) was achieved using a four - layer encoder – decoder QRNN with 320 units per layer , no dropout or regularization , and gradient rescaling to a maximum magnitude of 5 . Inputs were supplied to the encoder reversed , while the encoder convolutions were not masked . The first encoder layer used convolutional filter width , while the other encoder layers used .
Our experiments in § 6 confirm this : using as few as k = 4 eigenvectors per channel for patches of size m = 5 is sufficient . As our second speedup , we observe that the projection Qᵀp can be computed by a series of k convolutions between a channel image and each m×m filter reshaped from its corresponding eigenvector ( column of Q ) . This is possible because the covariance matrix Σ is shared across all patches per channel and hence the derived Q is likewise spatially invariant .
subsection : Submanifold Convolutional Networks We use a combination of VSC convolutions , strided SC convolutions , and sparse pooling operations to build sparse versions of the popular VGG , ResNet , and DenseNet convolutional networks . The blocks we use in our networks are presented in Figure [ reference ] .
The core idea is to equip each original convolutional layer with another low-cost collaborative layer ( LCCL ) , and the element-wise multiplication of the ReLU outputs of these two parallel layers produces the layer-wise output . The combined layer is potentially more discriminative than the original convolutional layer , and its inference is faster for two reasons : 1 ) the zero cells of the LCCL feature maps will remain zero after element-wise multiplication , and thus it is safe to skip the calculation of the corresponding high-cost convolution in the original convolutional layer , 2 ) LCCL is very fast if it is implemented as a 1 * 1 convolution or only a single filter shared by all channels . Extensive experiments on the CIFAR-10 , CIFAR-100 and ILSCRC-2012 benchmarks show that our proposed network structure can accelerate the inference process by 32 % on average with negligible performance drop .
In addition , the stacked local residual block ( LRB ) structure is used for non-linear mapping , which can effectively overcome the problem of image degradation . After extracting features , use 1 × 1 convolution to perform dimensional compression , and expand the dimensions after non-linear mapping , which can reduce the calculation amount of the model . In the reconstruction layer , deconvolution is used to enlarge the image to the required size .
In this letter , we proposed a novel method to extract convective clouds . We introduce a novel deep network using only $ 1 \times 1 $ convolution ( 3ONet ) to extract the spectral characteristics . We then combine a 3ONet with the symmetrical dense-shortcut deep fully convolutional networks ( SDFCNs ) with a double-stream fully convolutional network to extract convective clouds .
Unlike existing RGBT trackers , our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation , low illumination , background clutter and occlusion . In particular , we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution , and use 1x1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation . To model the interactions between RGB and thermal modalities , we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources .
The cropping is necessary due to the loss of border pixels in every convolution . At the final layer a 1x1 convolution is used to map each 64 - component feature vector to the desired number of classes . In total the network has 23 convolutional layers .
Using a 2k factorial experiment design , we evaluated the impact of several aspects such as database size , types of models and training parameters in the quality of automatic translation obtained . The results of the experiments were very promising and point to an initial superiority of the LightConv model in most of the evaluated scenarios .
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