We further show how one can estimate these probabilities , adapting a recent technique for noise estimation to the multi-class setting , and thus providing an end-to-end framework . Extensive experiments on MNIST , IMDB , CIFAR-10 , CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures & # x2014 ; stacking dense , convolutional , pooling , dropout , batch normalization , word embedding , LSTM and residual layers & # x2014 ; demonstrate the noise robustness of our proposals . Incidentally , we also prove that , when ReLU is the only non-linearity , the loss curvature is immune to class-dependent label noise . Normalization techniques such as Batch Normalization have been applied very successfully for training deep neural networks . Yet , despite its apparent empirical benefits , the reasons behind the success of Batch Normalization are mostly hypothetical . This effect prevails in deep single-channel linear convolutional networks , and we show that without channel normalization , gradient descent takes at least exponentially many steps to come close to an optimum . Contrary , with channel normalization , the gradients remain bounded , thus avoiding exploding gradients . For this , EDA signals are obtained from publically available online DEAP database . These signals are normalized using channel normalization and subjected to MCNN for event-related robust features and classification . K-fold cross validation is used to investigate the performance of classifier . In all cases , and contrary to common practice , our results indicate that channel normalization facilitates the best indexing performance . We predict that channel based normalization may improve indexing performance for many image indexing applications . It is apparent from our findings that only normalization is the effective approach while working within less epochs . Also , Dropout After Batch Normalization configuration results in smooth loss reduction . This paper introduces a new image smoothing filter based on a feed-forward convolutional neural network ( CNN ) in presence of impulse noise . This smoothing filter integrates a very deep architecture , a regularization method , and a batch normalization process . This fully integrated approach yields an effectively denoised and smoothed image yielding a high similarity measure with the original noise free image . We show that channel-based normalizations ( e.g. Layer Normalization and Group Normalization ) are unable to guarantee a far distance from elimination singularities , in contrast with Batch Normalization which by design avoids models from getting too close to them . To address this issue , we propose BatchChannel Normalization ( BCN ) , which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models . Unlike Batch Normalization , BCN is able to run in both large-batch and micro-batch training settings . At the same time , DC components in the modulation spectrum should be effectively removed . With context-independent models the channel normalization filter should have a flat phase response . Finally , for our connected digit recognition task it appeared that cepstrum mean subtraction and phase-corrected RASTA performed equally well for context-dependent and context-independent models when equal amounts of model parameters were used . In Squeeze-and-Excitation ( SE ) Networks , the channel relationships are implicitly learned by fully connected layers , and the SE block is integrated at the block-level . We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity . This lightweight layer incorporates a simple l2 normalization , enabling our transformation unit applicable to operator-level without much increase of additional parameters . To overcome the problems related with the long impulse responses produced by reverberation , we use a long time window ( high frequency resolution ) analysis during the channel normalization steps of the feature extraction process in automatic speech recognition ( ASR ) . After nor-malization , a trade between frequency and time resolution is used to increase the rate at which the time information is sampled ( short-time domain ) , yielding an appropriate domain to derive ASR features . Figure [ reference ] diagrams the two variants of the blocks . In both cases , we use layer normalization before the activation function , as it is well suited to sequence processing where computing the activation statistics over the following future tokens ( as would be done by batch normalization ) must be avoided . After a series of residual blocks of increased dilation , the network applies one more convolution and ReLU followed by a convolution and a final softmax layer . Network , inspired by , maps SVHN - trained ’s 128D representations to grayscale images . employs four blocks of deconvolution , batch - normalization , and ReLU , with a hyperbolic tangent terminal . The architecture of consists of four batch - normalized convolutional layers and employs ReLU . The loss layers are not shown in Table [ reference ] . The Batch Normalization Layers are inserted before every ReLU Layer to accelerate the convergence . We employ a Convolutional Layer and a Global Average Pooling Layer ( GAP ) at the end of network to let our network can fit different sizes of input images . It drops in a ‘ ‘ steps ’ ’ manner by a factor of . Following , batch normalization layers are refined after training . subsubsection : ImageNet - 1k dataset This is the first step towards understanding this phenomenon . We allow each convolutional layer to be stacked before a switchable normalization ( SN ) that learns to choose a normalizer from a pool of normalization methods . Through systematic experiments in ImageNet , COCO , Cityscapes , and ADE20 K , we answer three questions : ( a ) Is it useful to allow each normalization layer to select its own normalizer ? ( 3 ) We achieve the second best performance on CityPersons benchmark , i.e. 9.3 % log-average miss rate(MR ) on reasonable set , 8.7 % MR on partial set and 5.6 % MR on bare set , which shows an anchor-free and one-stage detector can still have high accuracy . ( 4 ) We explore some capabilities of Switchable Normalization which are not mentioned in its original paper . We address a learning-to-normalize problem by proposing Switchable Normalization ( SN ) , which learns to select different normalizers for different normalization layers of a deep neural network . SN employs three distinct scopes to compute statistics ( means and variances ) including a channel , a layer , and a minibatch . We address a learning-to-normalize problem by proposing Switchable Normalization ( SN ) , which learns to select different normalizers for different normalization layers of a deep neural network . SN employs three distinct scopes to compute statistics ( means and variances ) including a channel , a layer , and a minibatch . This study follows from the fact that most of the analysis requires nuclei detection as the starting phase for getting an insight into the underlying biological process and further diagnosis . The proposed architecture consists of a switch normalization layer , convolution layers , and inception layers ( concatenated 1x1 , 3x3 , and 5x5 convolution and the hybrid of a max and Hartley spectral pooling layer ) connected in the U-Net fashion for generating the image masks . This article also illustrates the model perception of image masks using activation maximization and filter map visualization techniques . We present a simple and general method to train a single neural network executable at different widths ( number of channels in a layer ) , permitting instant and adaptive accuracy-efficiency trade-offs at runtime . Instead of training individual networks with different width configurations , we train a shared network with switchable batch normalization . At runtime , the network can adjust its width on the fly according to on-device benchmarks and resource constraints , rather than downloading and offloading different models . This paper presents a scene categorization method that is invariant to affine transformations . We propose a new moment-based normalization algorithm to generate an output image that is independent of the position , rotation , shear , and scale of the input image . In the proposed approach , an affine transform matrix is determined subject to the normalized image satisfying a set of moment constraints . The classical idea of the principal axes is analyzed and extended to a more general definition . The relationship between moment-based normalization , moment invariants , and circular harmonics is established . Invariance properties of moments , as opposed to their recognition properties , are identified using a new class of normalization procedures . In transductive systems a local model is developed for every new input vector , based on some closest to this vector data from the training data set . The Weighted Data Normalization method ( WDN ) optimizes the data normalization range individually for each input variable of the system . A gradient descent al- gorithm is used for training the TWRBF model . The college was constructed test bank , through resource integration and accumulation . And the number of questions , coverage of the knowledge point , update normalization for test bank meets the operation requirements of examination system . But , it is a difficult problem to make sure randomness , rationality , balance of auto generating test paper . The landmark candidate regions detected by the ifBM model are analyzed by the RNN . New learning rules based on update normalization are developed to decrease learning time and increase system stability . The network also has the ability to learn new patterns with minimal retraining time . This paper presents the results for task 2 of ShAre/CLEF 2015 eHealth Evaluation Lab . We use BM25 as our base normalization method and Pseudo Relevance Feedback to retrieve information regarding patients ’ health and find the best results to expand the efficiency of information retrieval system . Participants in task 2 are provided with a collection of datasets focused on health web pages . Results on the YOHO corpus show that the use of MAP adaptation provides a relative improvement of 22.6 % in Equal Error Rate ( EER ) in comparison with Baum-Welch retraining and Maximum Likelihood Linear Regression ( MLLR ) adaptation . The newly proposed sub-word level T-Normalization procedures provide additional relative improvements , particularly for small cohorts , of up to 20 % in EER in comparison with the normal utterance-level T-Normalization . We have evaluated our method using two databases-TIMIT and NTT . Results show that the combination of frame and utterance level likelihood normalization in some cases reduces the equal error rate ( EER ) more than twice . In our method , we present a normalization function to map the float DC ( Discrete Cosine ) coefficients to integer domain . Thirdly , considering that the shape error and encryption/decryption computation cost are contradictory in the normalization mapping , we formulate the contradiction as a multi-objective optimization problem . And then , we propose a multi-objective solution to find an optimized mapping range for encryption/decryption efficiently . The proposed approach is extended to multi-look and sequential ATR where new observation profiles are recursively combined probabilistically with previous steps to update ATR results , which is useful for simultaneous recognition and tracking of moving targets . An HRR-specific profile normalization scheme is presented to satisfy matched filter requirements . Classification performance of the proposed method has been compared with a linear least-squares method and hidden Markov model ( HMM ) approach using MSTAR data collection . A new nonlinear multiscale edge enhancement technique is presented here to sharpen the image edge based on the wavelet coefficients without enhancing the noise as well . The visual group normalization technique ( VGN ) , as a natural extensions of earlier normalization techniques for noisy image restoration , is used to normalize the multiscale wavelet coefficients , remove perceptual redundancy , as well as to improve the visualization of the important diagnostic features for digital mammograms . Without prior knowledge of the ` true ' spatial distribution of the signal , excellent enhancement results are obtained by the present techniques . Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity – specificity ROC curve . This evaluation compares three different EEG normalization methods , namely a fixed baseline buffer ( FB ) , the median decaying memory ( MDM ) approach , and our novelty median decaying memory ( Novelty-MDM ) method . It is demonstrated that MDM did not improve overall performance compared to FB ( p < 0.27 ) , partly because seizure like episodes were included in the baseline . For training we used an - greedy policy with being annealed linearly from to over the first 1 m timesteps . subsection : Gradient normalization in bootstrap heads Most literature in deep RL for Atari focuses on learning the best single evaluation policy , with particular attention to whether this above or below human performance . This is unusual for the RL literature , which typically focuses upon cumulative or final performance . Bootstrapped DQN makes significant improvements to the cumulative rewards of DQN on Atari , as we display in Figure [ reference ] , while the peak performance is much more We found that using bootstrapped DQN without gradient normalization on each head typically learned even faster than our implementation with rescaling , but it was somewhat prone to premature and suboptimal convergence . We present an example of this phenomenon in Figure [ reference ] . Unless mentioned otherwise , for CIFAR we follow the image preprocessing of with ZCA whitening . However , for some CIFAR experiments we instead use simple mean / std normalization such that we can directly compare with and other ResNet related works that make use of this type of preprocessing . In the following we describe our findings w.r.t . However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size . Due to these optimization challenges in RNNs , the application of batch normalization and its variants ( layer normalization , recurrent batch normalization , recurrent normalization propagation ) have not been as successful as their counterparts in feed - forward networks , although they do considerably provide performance gains . Similarly , naive application of dropout has been shown to be ineffective in RNNs . This scheme bears some resemblance to the local contrast normalization scheme of Jarrett et al . [ 11 ] , but ours would be more correctly termed “ brightness normalization ” , since we do not subtract the mean activity . Response normalization reduces our top - 1 and top - 5 Therefore , Here the softmax matrix M is decomposed into the weights for word feature and the weights for the visual feature whereas . is the response of the answer class before softmax normalization . Denote the response as the contribution from question words and as the contribution from the image contents . paragraph : Non - linear rectification : We also evaluate the impact of the parameter of the non - linear rectification obtained by applying power normalization , see Eq . ( [ reference ] ) . A fully connected layer is followed by the final pooling layer to generate the original quality score . After that , the origin scores of all images in a set are sent to sigmoid layer and group L1 - normalization layer to generate the final scores . For QAN split at Pool3 , we will add a block containing three 1 - stride convolution layer and a 2 - stride pooling layer at the beginning of quality generation unit . However , SN uses softmax function to learn importance ratios to combine normalizers , leading to redundant computations compared to a single normalizer . This work addresses this issue by presenting Sparse Switchable Normalization ( SSN ) where the importance ratios are constrained to be sparse . Unlike $ \ell_1 $ and $ \ell_0 $ constraints that impose difficulties in optimization , we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax , which is a sparse version of softmax .
We further show how one can estimate these probabilities , adapting a recent technique for noise estimation to the multi-class setting , and thus providing an end-to-end framework . Extensive experiments on MNIST , IMDB , CIFAR-10 , CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures & # x2014 ; stacking dense , convolutional , pooling , dropout , batch normalization , word embedding , LSTM and residual layers & # x2014 ; demonstrate the noise robustness of our proposals . Incidentally , we also prove that , when ReLU is the only non-linearity , the loss curvature is immune to class-dependent label noise .
Normalization techniques such as Batch Normalization have been applied very successfully for training deep neural networks . Yet , despite its apparent empirical benefits , the reasons behind the success of Batch Normalization are mostly hypothetical .
This effect prevails in deep single-channel linear convolutional networks , and we show that without channel normalization , gradient descent takes at least exponentially many steps to come close to an optimum . Contrary , with channel normalization , the gradients remain bounded , thus avoiding exploding gradients .
For this , EDA signals are obtained from publically available online DEAP database . These signals are normalized using channel normalization and subjected to MCNN for event-related robust features and classification . K-fold cross validation is used to investigate the performance of classifier .
In all cases , and contrary to common practice , our results indicate that channel normalization facilitates the best indexing performance . We predict that channel based normalization may improve indexing performance for many image indexing applications .
It is apparent from our findings that only normalization is the effective approach while working within less epochs . Also , Dropout After Batch Normalization configuration results in smooth loss reduction .
This paper introduces a new image smoothing filter based on a feed-forward convolutional neural network ( CNN ) in presence of impulse noise . This smoothing filter integrates a very deep architecture , a regularization method , and a batch normalization process . This fully integrated approach yields an effectively denoised and smoothed image yielding a high similarity measure with the original noise free image .
We show that channel-based normalizations ( e.g. Layer Normalization and Group Normalization ) are unable to guarantee a far distance from elimination singularities , in contrast with Batch Normalization which by design avoids models from getting too close to them . To address this issue , we propose BatchChannel Normalization ( BCN ) , which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models . Unlike Batch Normalization , BCN is able to run in both large-batch and micro-batch training settings .
At the same time , DC components in the modulation spectrum should be effectively removed . With context-independent models the channel normalization filter should have a flat phase response . Finally , for our connected digit recognition task it appeared that cepstrum mean subtraction and phase-corrected RASTA performed equally well for context-dependent and context-independent models when equal amounts of model parameters were used .
In Squeeze-and-Excitation ( SE ) Networks , the channel relationships are implicitly learned by fully connected layers , and the SE block is integrated at the block-level . We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity . This lightweight layer incorporates a simple l2 normalization , enabling our transformation unit applicable to operator-level without much increase of additional parameters .
To overcome the problems related with the long impulse responses produced by reverberation , we use a long time window ( high frequency resolution ) analysis during the channel normalization steps of the feature extraction process in automatic speech recognition ( ASR ) . After nor-malization , a trade between frequency and time resolution is used to increase the rate at which the time information is sampled ( short-time domain ) , yielding an appropriate domain to derive ASR features .
Figure [ reference ] diagrams the two variants of the blocks . In both cases , we use layer normalization before the activation function , as it is well suited to sequence processing where computing the activation statistics over the following future tokens ( as would be done by batch normalization ) must be avoided . After a series of residual blocks of increased dilation , the network applies one more convolution and ReLU followed by a convolution and a final softmax layer .
Network , inspired by , maps SVHN - trained ’s 128D representations to grayscale images . employs four blocks of deconvolution , batch - normalization , and ReLU , with a hyperbolic tangent terminal . The architecture of consists of four batch - normalized convolutional layers and employs ReLU .
The loss layers are not shown in Table [ reference ] . The Batch Normalization Layers are inserted before every ReLU Layer to accelerate the convergence . We employ a Convolutional Layer and a Global Average Pooling Layer ( GAP ) at the end of network to let our network can fit different sizes of input images .
It drops in a ‘ ‘ steps ’ ’ manner by a factor of . Following , batch normalization layers are refined after training . subsubsection : ImageNet - 1k dataset
This is the first step towards understanding this phenomenon . We allow each convolutional layer to be stacked before a switchable normalization ( SN ) that learns to choose a normalizer from a pool of normalization methods . Through systematic experiments in ImageNet , COCO , Cityscapes , and ADE20 K , we answer three questions : ( a ) Is it useful to allow each normalization layer to select its own normalizer ?
( 3 ) We achieve the second best performance on CityPersons benchmark , i.e. 9.3 % log-average miss rate(MR ) on reasonable set , 8.7 % MR on partial set and 5.6 % MR on bare set , which shows an anchor-free and one-stage detector can still have high accuracy . ( 4 ) We explore some capabilities of Switchable Normalization which are not mentioned in its original paper .
We address a learning-to-normalize problem by proposing Switchable Normalization ( SN ) , which learns to select different normalizers for different normalization layers of a deep neural network . SN employs three distinct scopes to compute statistics ( means and variances ) including a channel , a layer , and a minibatch .
We address a learning-to-normalize problem by proposing Switchable Normalization ( SN ) , which learns to select different normalizers for different normalization layers of a deep neural network . SN employs three distinct scopes to compute statistics ( means and variances ) including a channel , a layer , and a minibatch .
This study follows from the fact that most of the analysis requires nuclei detection as the starting phase for getting an insight into the underlying biological process and further diagnosis . The proposed architecture consists of a switch normalization layer , convolution layers , and inception layers ( concatenated 1x1 , 3x3 , and 5x5 convolution and the hybrid of a max and Hartley spectral pooling layer ) connected in the U-Net fashion for generating the image masks . This article also illustrates the model perception of image masks using activation maximization and filter map visualization techniques .
We present a simple and general method to train a single neural network executable at different widths ( number of channels in a layer ) , permitting instant and adaptive accuracy-efficiency trade-offs at runtime . Instead of training individual networks with different width configurations , we train a shared network with switchable batch normalization . At runtime , the network can adjust its width on the fly according to on-device benchmarks and resource constraints , rather than downloading and offloading different models .
This paper presents a scene categorization method that is invariant to affine transformations . We propose a new moment-based normalization algorithm to generate an output image that is independent of the position , rotation , shear , and scale of the input image . In the proposed approach , an affine transform matrix is determined subject to the normalized image satisfying a set of moment constraints .
The classical idea of the principal axes is analyzed and extended to a more general definition . The relationship between moment-based normalization , moment invariants , and circular harmonics is established . Invariance properties of moments , as opposed to their recognition properties , are identified using a new class of normalization procedures .
In transductive systems a local model is developed for every new input vector , based on some closest to this vector data from the training data set . The Weighted Data Normalization method ( WDN ) optimizes the data normalization range individually for each input variable of the system . A gradient descent al- gorithm is used for training the TWRBF model .
The college was constructed test bank , through resource integration and accumulation . And the number of questions , coverage of the knowledge point , update normalization for test bank meets the operation requirements of examination system . But , it is a difficult problem to make sure randomness , rationality , balance of auto generating test paper .
The landmark candidate regions detected by the ifBM model are analyzed by the RNN . New learning rules based on update normalization are developed to decrease learning time and increase system stability . The network also has the ability to learn new patterns with minimal retraining time .
This paper presents the results for task 2 of ShAre/CLEF 2015 eHealth Evaluation Lab . We use BM25 as our base normalization method and Pseudo Relevance Feedback to retrieve information regarding patients ’ health and find the best results to expand the efficiency of information retrieval system . Participants in task 2 are provided with a collection of datasets focused on health web pages .
Results on the YOHO corpus show that the use of MAP adaptation provides a relative improvement of 22.6 % in Equal Error Rate ( EER ) in comparison with Baum-Welch retraining and Maximum Likelihood Linear Regression ( MLLR ) adaptation . The newly proposed sub-word level T-Normalization procedures provide additional relative improvements , particularly for small cohorts , of up to 20 % in EER in comparison with the normal utterance-level T-Normalization .
We have evaluated our method using two databases-TIMIT and NTT . Results show that the combination of frame and utterance level likelihood normalization in some cases reduces the equal error rate ( EER ) more than twice .
In our method , we present a normalization function to map the float DC ( Discrete Cosine ) coefficients to integer domain . Thirdly , considering that the shape error and encryption/decryption computation cost are contradictory in the normalization mapping , we formulate the contradiction as a multi-objective optimization problem . And then , we propose a multi-objective solution to find an optimized mapping range for encryption/decryption efficiently .
The proposed approach is extended to multi-look and sequential ATR where new observation profiles are recursively combined probabilistically with previous steps to update ATR results , which is useful for simultaneous recognition and tracking of moving targets . An HRR-specific profile normalization scheme is presented to satisfy matched filter requirements . Classification performance of the proposed method has been compared with a linear least-squares method and hidden Markov model ( HMM ) approach using MSTAR data collection .
A new nonlinear multiscale edge enhancement technique is presented here to sharpen the image edge based on the wavelet coefficients without enhancing the noise as well . The visual group normalization technique ( VGN ) , as a natural extensions of earlier normalization techniques for noisy image restoration , is used to normalize the multiscale wavelet coefficients , remove perceptual redundancy , as well as to improve the visualization of the important diagnostic features for digital mammograms . Without prior knowledge of the ` true ' spatial distribution of the signal , excellent enhancement results are obtained by the present techniques .
Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity – specificity ROC curve . This evaluation compares three different EEG normalization methods , namely a fixed baseline buffer ( FB ) , the median decaying memory ( MDM ) approach , and our novelty median decaying memory ( Novelty-MDM ) method . It is demonstrated that MDM did not improve overall performance compared to FB ( p < 0.27 ) , partly because seizure like episodes were included in the baseline .
For training we used an - greedy policy with being annealed linearly from to over the first 1 m timesteps . subsection : Gradient normalization in bootstrap heads Most literature in deep RL for Atari focuses on learning the best single evaluation policy , with particular attention to whether this above or below human performance . This is unusual for the RL literature , which typically focuses upon cumulative or final performance . Bootstrapped DQN makes significant improvements to the cumulative rewards of DQN on Atari , as we display in Figure [ reference ] , while the peak performance is much more We found that using bootstrapped DQN without gradient normalization on each head typically learned even faster than our implementation with rescaling , but it was somewhat prone to premature and suboptimal convergence . We present an example of this phenomenon in Figure [ reference ] .
Unless mentioned otherwise , for CIFAR we follow the image preprocessing of with ZCA whitening . However , for some CIFAR experiments we instead use simple mean / std normalization such that we can directly compare with and other ResNet related works that make use of this type of preprocessing . In the following we describe our findings w.r.t .
However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size . Due to these optimization challenges in RNNs , the application of batch normalization and its variants ( layer normalization , recurrent batch normalization , recurrent normalization propagation ) have not been as successful as their counterparts in feed - forward networks , although they do considerably provide performance gains . Similarly , naive application of dropout has been shown to be ineffective in RNNs .
This scheme bears some resemblance to the local contrast normalization scheme of Jarrett et al . [ 11 ] , but ours would be more correctly termed “ brightness normalization ” , since we do not subtract the mean activity . Response normalization reduces our top - 1 and top - 5
Therefore , Here the softmax matrix M is decomposed into the weights for word feature and the weights for the visual feature whereas . is the response of the answer class before softmax normalization . Denote the response as the contribution from question words and as the contribution from the image contents .
paragraph : Non - linear rectification : We also evaluate the impact of the parameter of the non - linear rectification obtained by applying power normalization , see Eq . ( [ reference ] ) .
A fully connected layer is followed by the final pooling layer to generate the original quality score . After that , the origin scores of all images in a set are sent to sigmoid layer and group L1 - normalization layer to generate the final scores . For QAN split at Pool3 , we will add a block containing three 1 - stride convolution layer and a 2 - stride pooling layer at the beginning of quality generation unit .
However , SN uses softmax function to learn importance ratios to combine normalizers , leading to redundant computations compared to a single normalizer . This work addresses this issue by presenting Sparse Switchable Normalization ( SSN ) where the importance ratios are constrained to be sparse . Unlike $ \ell_1 $ and $ \ell_0 $ constraints that impose difficulties in optimization , we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax , which is a sparse version of softmax .
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach Towards a Theoretical Understanding of Batch Normalization Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolution Neural Network Using colour for image indexing Effect of Dropout and Batch Normalization in Siamese Network for Face Recognition High Impulse Noise Intensity Removal in Natural Images Using Convolutional Neural Network Rethinking Normalization and Elimination Singularity in Neural Networks Channel normalization techniques for automatic speech recognition over the telephone Gated Channel Transformation for Visual Recognition Multiresolution channel normalization for ASR in reverberant environments Neural Machine Translation in Linear Time Unsupervised Cross-Domain Image Generation Pose-Driven Deep Convolutional Model for Person Re-identification Dual Path Networks Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct? Adapted Center and Scale Prediction: More Stable and More Accurate Differentiable Learning-to-Normalize via Switchable Normalization Switchable Normalization for Learning-to-Normalize Deep Representation Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images Slimmable Neural Networks Affine-invariant scene categorization Image Normalization by Complex Moments TWRBF - Transductive RBF Neural Network with Weighted Data Normalization Research on Auto-generating Test Paper based on Constrains of Genetic Algorithm Outdoor landmark recognition using fractal based vision and neural networks York University at CLEF 2015 eHealth : Medical Document Retrieval MAP and sub-word level t-norm for text-dependent speaker recognition Speaker verification using frame and utterance level likelihood normalization An asymmetric and optimized encryption method to protect the confidentiality of 3D mesh model Eigen-Template-Based HRR-ATR with Multi-Look and Time-Recursion Mammogram feature analysis system using DAF wavelet Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection Deep Exploration via Bootstrapped DQN Wide Residual Networks Fraternal Dropout ImageNet classification with deep convolutional neural networks Simple Baseline for Visual Question Answering DeepMatching: Hierarchical Deformable Dense Matching Quality Aware Network for Set to Set Recognition SSN: Learning Sparse Switchable Normalization via SparsestMax
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EFMI-STC CCWC EUROSPEECH ArXiv ICLR 2017 IEEE International Conference on Computer Vision (ICCV) NIPS ICIP ICONIP IROS CLEF (Working Notes) INTERSPEECH ICASSP Visual Information Processing NIPS BMVC ICLR CACM ArXiv International Journal of Computer Vision 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR
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