As an indispensable component , Batch Normalization ( BN ) has successfully improved the training of deep neural networks ( DNNs ) with mini-batches , by normalizing the distribution of the internal representation for each hidden layer . However , the effectiveness of BN would diminish with scenario of micro-batch ( e.g. , less than 10 samples in a mini-batch ) , since the estimated statistics in a mini-batch are not reliable with insufficient samples . Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization . In this work , we show that a commonly used deep network , which uses convolution , batch normalization , reLU , max-pooling , and sub-sampling pipeline , possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights . We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold . We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normalization , in order to compute the most informative image features for our task . Additionally , we propose a new attention mechanism derived from this original idea , where the attention model for the visual input is conditioned on the source text encoder representations . The problem of approximating the widely used ReLU non-linearity is then considered . An half-wave Gaussian quantizer ( HWGQ ) is proposed for forward approximation and shown to have efficient implementation , by exploiting the statistics of of network activations and batch normalization operations . To overcome the problem of gradient mismatch , due to the use of different forward and backward approximations , several piece-wise backward approximators are then investigated . Conditional Batch Normalization ( CBN ) has proved to be an effective tool for visual question answering . However , previous CBN approaches fuse the linguistic information into image features via a simple affine transformation , thus they have struggled on compositional reasoning and object counting in images . In this work we propose a novel GAN architecture which is able to generate photo-realistic images with a specified aesthetic quality by conditioning on both semantic and aesthetic information . To condition the generator , we propose a modified conditional batch normalization layer . To condition the discriminator , we use a joint probabilistic model of semantics and aesthetics to estimate the compatibility between an image ( either real or generated ) and the conditioning variable . Different from the IDE proposed in [ reference ] , we discard the last 1000 - dimensional classification layer and add two fully connected ( FC ) layers . The output of the first FC layer has 1024 dimensions named as " FC - 1024 " , followed by batch normalization [ reference ] , ReLU and Dropout [ reference ] . In all experiments , we use the Adam optimization algorithm adam to optimize the parameters of the generative models and variational approximations . Batch normalization batchnorm is implemented for convolutional layers and we always use a batch size of . We primarily use the Theano theano framework with the Lasagne lasagne library , but the final experiments with face data were done using Tensorflow tensorflow . Layer normalization ( LayerNorm ) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix . However , the computational overhead introduced by LayerNorm makes these improvements expensive and significantly slows the underlying network , e.g. RNN in particular . While the new models completely depart from the original recurrent architecture , we decided to investigate how to make RNNs more efficient . In this work , we propose a new recurrent NMT architecture , called Simple Recurrent NMT , built on a class of fast and weakly-recurrent units that use layer normalization and multiple attentions . Our experiments on the WMT14 English-to-German and WMT16 English-Romanian benchmarks show that our model represents a valid alternative to LSTMs , as it can achieve better results at a significantly lower computational cost . A natural compromise is to normalize features by local context , while also taking into account group level information . In this pa- per , we propose Local Context Normalization ( LCN ) : a normalization layer where every feature is normalized based on a window around it and the filters in its group . We propose an algorithmic solution to make LCN efficient for arbitrary window sizes , even if every point in the image has a unique window . In this paper , we introduce a novel deep model called Deep Sparse Coding Network ( DeepSCNet ) for Image classification . This new model includes four types of layers : the Sparse-coding layer , the Pooling layer , the Normalization layer and the Map reduction layer . The Sparse-coding laying does the general linear coding work for local patch within the receptive field . To tackle problems above , we generalize effective learning ability of multilayer perception and propose a hybrid recommendation method with multilayer perception which jointly learns deep representations of users and items from side information as well as rating matrix and interaction function between user and item latent factors . We also propose to add a data normalization layer after combining two vectors coming from different magnitudes . Extensive experiments on two real world datasets show that our model outperforms state-of-the-art algorithms . A squeeze-and-excitation module is utilized after each inception module to learn the weights of attention mechanism . A feature normalization layer is also added before the softmax layer , and compared with different discriminant loss functions including contrastive loss , triplet loss and center loss . Experimental results on both CASIA HWDB offline handwritten Chinese character datasets and historical Chinese samples show that the proposed method achieves improved recognition performance . This was shown to work well in the recurrent setting . In this paper we propose a unified view of normalization techniques , as forms of divisive normalization , which includes layer and batch normalization as special cases . Our second contribution is the finding that a small modification to these normalization schemes , in conjunction with a sparse regularizer on the activations , leads to significant benefits over standard normalization techniques . Moreover , it enables users to generate various stylish descriptions by plugging in style-specific parameters to include new styles into the existing model . We achieve this capability via a novel layer normalization layer design , which we will refer to as the Domain Layer Norm ( DLN ) . Extensive experimental validation and user study on various stylish image description generation tasks are conducted to show the competitive advantages of the proposed model . In this paper , we aim at understanding the behaviors of Transformers in the large-scale speech recognition setting , where we have used around 65,000 hours of training data . We investigated various aspects on scaling up Transformers , including model initialization , warmup training as well as different Layer Normalization strategies . In the streaming condition , we compared the widely used attention mask based future context lookahead approach to the Transformer-XL network . To address this problem , a body symmetry and part-locality-guided direct nonparametric deep feature enhancement ( DNDFE ) method is proposed in this article . Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons , the proposed method designs two nonparametric layers , namely , the body symmetry average pooling and local normalization layers , to construct a DNDFE module to well explore the body symmetry and part locality properties . The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance . Similarly to prior work , our method learns by applying differentiable warping to frames and comparing the result to adjacent ones , but it provides several improvements : We address occlusions geometrically and differentiably , directly using the depth maps as predicted during training . We introduce randomized layer normalization , a novel powerful regularizer , and we account for object motion relative to the scene . To the best of our knowledge , our work is the first to learn the camera intrinsic parameters , including lens distortion , from video in an unsupervised manner , thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale . The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) . We also include the FastSent variant “ FastSent + AE ” and the SkipThought - LN version that uses layer normalization . We report results from models trained on supervised data in the third group , and also report some results of supervised methods trained directly on each task for comparison with transfer learning approaches . [ reference ][ reference ] . We also apply layer normalization [ reference ] ) to our models . For all of the character - level language modelling experiments , we apply the same procedure , but only change the number of hidden units , mini - batch size and the initial learning rate . subsection : Convolutional modules To perform local computation , we use modules of convolutions with ReLU non - linearities and layer normalization . A module of convolutions gets as input a tensor of shape [ sequence length , feature channels ] and returns a tensor of the same shape . We use 5 different aspect ratios { , , 1 , 2 , 3 } for all layers except that we ignore { , 3 } for conv3_3 . Following SSD , we also use normalization layer on conv3_3 , conv4_3 , and conv5_3 and initialize the norm 40 . For Fast R - CNN part , we extract features from conv3_3 , conv5_3 , and conv8 for each region proposal and concatenate all the features to predict class scores and further adjust the proposals . In this paper we present the analysis of different representation techniques of the character images for extracting shape based features using modified signature method . An adaptive normalization is proposed that retains the aspect ratio of the character which is subjected to the fact that preservation of aspect ratio enhances the recognition process . Signature is generated from direction angle of the pixels where the pixels were taken from boundary , difference and skeletonized image of the character . In this paper , an improved algorithm for enhancement of fingerprint image is proposed on the basis of the image normalization and Gabor filter Firstly , the adaptive normalization based on block processing is suggested for improvement of fingerprint images . An input image is partitioned into sub-blocks with the size of K /spl times/ L at first and the region of interest ( ROl ) of the fingerprint image is acquired The parameters for the image normalization are adaptively determined according to the statistics of each block . We propose a novel domain adaptation method for deep learning that combines adaptive batch normalization to produce a common feature-space between domains and label transfer with subspace alignment on deep features . The first step of our method automatically conditions the features from the source/target domain to have similar statistical distributions by normalizing the activations in each layer of our network using adaptive batch normalization . We then examine the clustering properties of the normalized features on a manifold to determine if the target features are well suited for the second of our algorithm , label-transfer . Recently , unsupervised exemplar-based image-to-image translation , conditioned on a given exemplar without the paired data , has accomplished substantial advancements . In order to transfer the information from an exemplar to an input image , existing methods often use a normalization technique , e.g. , adaptive instance normalization , that controls the channel-wise statistics of an input activation map at a particular layer , such as the mean and the variance . Meanwhile , style transfer approaches similar task to image translation by nature , demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style . Without sacrificing image structure details , it enables the arbitrary style-content transfer . Second , accelerated with Adaptive Instance Normalization block , our framework achieved real-time speed required in the clinical US scanning . Third , an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other . However , prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations , which could become a performance bottleneck on edge devices with insufficient computational power . To address this problem , we propose an inference accelerator for content generation ( ACG-Engine ) aimed to support the fundamental operations of generative networks , including convolution layers , deconvolution layers , specifically instance normalization layer . We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness , so that it can be efficiently mapped to the classic 2D processing element array . We propose two modifications to these architectures and systematically compare them to mel-filterbanks , on the Wall Street Journal dataset . The first modification is the addition of an instance normalization layer , which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks . The second one relates to the low-pass filter used in these approaches . On the other hand , the shapes of the Stage 2 polynomial kernels have also been shown to oscillate after a certain threshold within the standard normalized input space of [ -1,1 ] . A simple adaptive data normalization approach is therefore proposed to confine the data to the threshold window where these kernels exhibit the sought after ideal shape characteristics , hence eliminate the possibility of any data point to be located outside the range where these oscillations are observed . The implementation of the adaptive data normalization approach accordingly leads to a more accurate calculation of similarity measures and improves the classification performance . When compared to the standard normalized input space , experimental results ( performed on the Stage 2 kernels ) demonstrate the effectiveness of the proposed adaptive data normalization approach , with an average accuracy improvement factor of 11.772 % , depending on the dataset and the polynomial function utilized . Finally , a new perspective is also introduced whereby the utilization of orthogonal polynomials is perceived as a way of transforming the input space to another vector space , of the same dimensionality as the input space , prior to the kernel calculation step . Due to the lack of training data in Asian face , a lot of well-known datasets , such as MS-Celeb-1 M , VGG Face and CASIA-Web Face that mainly consist of Caucasians , are applied to verify in Asians directly , which results in poor performance . To address this issue from the perspective of domain adaptation , in this paper , we propose a conceptually simple but novel method named Adaptive Instance Batch Normalization ( Ada-IBN ) . We first develop the method that transferring the knowledge from source to target domain to alleviate the discrepancy while increases the generalization capacity of CNNs . To cope with larger differences between the target line art image and reference color images , our architecture utilizes non-local similarity matching to determine the region correspondences between the target image and the reference images , which are used to transform the local color information from the references to the target . To ensure global color style consistency , we further incorporate Adaptive Instance Normalization ( AdaIN ) with the transformation parameters obtained from a style embedding vector that describes the global color style of the references , extracted by an embedder . The temporal constraint network takes the reference images and the target image together in chronological order , and learns the spatiotemporal features through 3D convolution to ensure the temporal consistency of the target image and the reference image . In this paper , we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time . At the heart of our method is a novel adaptive instance normalization ( AdaIN ) layer that aligns the mean and variance of the content features with those of the style features . Our method achieves speed comparable to the fastest existing approach , without the restriction to a pre-defined set of styles . Unlike previous attention-based method which can not handle the geometric changes between domains , our model can translate both images requiring holistic changes and images requiring large shape changes . Moreover , our new AdaLIN ( Adaptive Layer-Instance Normalization ) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets . Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters . An image representation is assumed to comprise both content information which is shared across domains and style information specific to one domain . By applying exemplar-based adaptive instance normalization to the shared content representation , EG-UNIT manages to transfer the style information in the target domain to the source domain . Experimental results on various datasets show that EG-UNIT can indeed translate the source image to diverse instances in the target domain with semantic consistency . It is shown that , although the four flux normalization methods S(max ) , S(median ) , S(mean ) and S(unit ) belong to the same limit efficiency type , on the spectra with usual observing scale , S(max ) and S(median ) are much more efficient than S(mean ) and S(unit ) , and S(unit ) is the most inefficient one . This work is helpful for choosing the appropriate flux normalization method based on the size of spectra database and the scientific needs in automatic spectra analysis . GN 's computation is independent of batch sizes , and its accuracy is stable in a wide range of batch sizes . On ResNet-50 trained in ImageNet , GN has 10.6 % lower error than its BN counterpart when using a batch size of 2 ; when using typical batch sizes , GN is comparably good with BN and outperforms other normalization variants . Moreover , GN can be naturally transferred from pre-training to fine-tuning . On ResNet50 trained in ImageNet , KN has 3.4 % lower error than its BN counterpart when using a batch size of 4 ; Even when using typical batch sizes , KN still maintains an advantage over BN while other BN variants suffer a performance degradation . Moreover , KN can be naturally generalized to many existing normalization variants to obtain gains , e.g. equipping Group Normalization with Group Kalman Normalization ( GKN ) . KN can outperform BN and its variants for large scale object detection and segmentation task in COCO 2017 . ScDNA-seq data is , however highly sparse and noisy due to the biases and artifacts that are introduced during the library preparation and sequencing procedure . Here , we propose SCOPE , a normalization and copy number estimation method for scDNA-seq data . The main features of SCOPE include : ( i ) a Poisson latent factor model for normalization , which borrows information across cells and regions to estimate bias , using negative control cells identified by cell-specific Gini coefficients ; ( ii ) modeling of GC content bias using an expectation-maximization algorithm embedded in the normalization step , which accounts for the aberrant copy number changes that deviate from the null distributions ; and ( iii ) a cross-sample segmentation procedure to identify breakpoints that are shared across cells from the same subclone . Whole-genome single-cell DNA sequencing ( scDNA-seq ) enables characterization of copy-number profiles at the cellular level . We propose SCOPE , a normalization and copy-number estimation method for the noisy scDNA-seq data . SCOPE 's main features include the following : ( 1 ) a Poisson latent factor model for normalization , which borrows information across cells and regions to estimate bias , using in silico identified negative control cells ; ( 2 ) an expectation-maximization algorithm embedded in the normalization step , which accounts for the aberrant copy-number changes and allows direct ploidy estimation without the need for post hoc adjustment ; and ( 3 ) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background . A hidden Markov model segments the word in a way that optimizes the global word score , using a dictionary in the process . A geometrical normalization scheme and a fast but efficient dictionary search are also presented . Trained on 20 k words from 59 writers , using a 25 k-word dictionary , our system reached recognition rates of 89 % for characters and 80 % for words on test data from a disjoint set of writers . The invention discloses a correlation-quality sequencing image retrieval method based on tag retrieval . The correlation-quality sequencing image retrieval method comprises the following steps of : firstly , automatically sequencing social images according to the correlation of images and tags , fusing the vision consistency among the images and the vision consistency of the semanteme correlation among the images and the tags by utilizing a normalization frame , and solving an optimization problem by utilizing an iteration algorithm so as to obtain a correlation sequence ; and selecting the brightness , the contrast ratio and the color variety of the images to be used as quality characteristics of the images to balance previously returned images , overlaying correlation scores and quality scores by utilizing a linear model , and re-sequencing the images according to a final total score , and therefore , a correlation-quality sequencing algorithm is realized . There are some internal and external sources increasing the influence of the phase noise . This paper investigates these sources , such as coupling between transmit and receive paths through the radar components , effects of targets having large RCS and surfaces of the radar platform in the radar operation zone generating unwanted echoes and proposes methods like RPC , DLC , windowing and video normalization to reduce the effect of this problem . Experimental results are also presented in this paper . The invention relates to a fish identification method and system based on wavelet packet multi-scale information entropy . The method , based on an active sonar identification strategy , comprises step 101 ) : sending a sound signal to an underwater fish body and obtaining an echo signal backscattered from the underwater fish body ; step 102 ) : performing a filtering and an amplitude normalization preprocessing on the collected echo data of the fish body ; step 103 ) : extracting features of the preprocessed echo signal with a specific method being as follows : performing a wavelet packet multi-scale decomposition on the preprocessed echo signal to obtain a wavelet packet coefficient , and reconstituting the wavelet packet coefficient to obtain a time-domain signal in each frequency band of the fish body echo signal , and then extracting information entropy of the time-domain signal obtained through reconstituting the wavelet packet coefficient , wherein the information entropy constitutes a feature quantity ; and step 104 ) : inputting the feature quantity into a classifier to perform a classification treatment so that kind identification of the underwater fish body is completed . This paper presents a shot boundary detection method based on the global decision tree that allows for extraction of boundaries of high variations occurring due to camera breaks from frame difference values . For a start , difference values between frames are calculated through local X2-histogram and normalization . Next , the distances between difference values are calculated through normalization . This paper describes the development , implementation and validation of an automatic speaker recognition system on an iPad tablet . A score normalization approach , referred as Nearest Neighbor Normalization ( 3N ) , is applied in order to improve the baseline speaker verification system . The system is evaluated on the MOBIO corpus and results show an absolute improvement of the HTER by more than 4 % when the score normalization is performed . The proposed approach was compared with three different approaches using the same neural network – one approach with no data augmentation , one with regularization not specific to visual data ( dropout and weight decay ) and one with extensive data augmentation specific for images . We conclude that by using the proposed set of augmentation techniques , including novel perturbed normalization , we postponed overfitting , which resulted in a better generalized model . The results were illustrated in Figure [ reference ] . If one removed the batch normalization layers from the generator , the DCGAN would collapse without producing any face images . On the contrary , the LS - GAN still performed very well even if these batch normalization layers were removed , and there was no perceived deterioration or mode collapse of the generated images . Fig . [ reference ] illustrates the basic building block of a Residual Network which consists of multiple convolutional and Batch Normalization layers . The identity transformation , is used to reduce the dimensions of to match those of . Network architecture . Input images in each template are first passed through a convolutional neural network ( e.g. ResNet - 50 or SENet - 50 with an additional FC layer and L2 - normalization ) to produce a face descriptor per image . The descriptors are aggregated into a single fixed - length vector using the GhostVLAD layer . The first term corresponds to the soft - assignment weight of the input vector x i for cluster k , while the second term computes the residual between the vector and the cluster centre . The final output is obtained by performing L2 normalization . GhostVLAD . subsection : Gating In order to analyze whether a hard ( discrete ) choice through gating will outperform our normalization strategy , we experimented with using gates instead of the proposed concatenation - addition strategy . Specifically , we replaced the summation in Equation ( [ reference ] ) by a gating structure that sums up the contributions of the top branches with the highest probabilities . Figure 9 shows the distribution of critic weight singular values , like Figure 2 , at more layers . Figure 11 and Table 2 show results for the spectral normalization variants considered in the experiments . MMDGAN , with neither spectral normalization nor a gradient penalty , did surprisingly well in this case , though it fails badly in other situations . The most straightforward method of aggregating normalized scores is to compute their average . Without perfect score normalization , however , score averages tend to be heavily influenced by games such as Zaxxon for which baseline scores are high . Averaging inter - algorithm scores obviates this issue as all scores are bounded between 0 and 1 .
As an indispensable component , Batch Normalization ( BN ) has successfully improved the training of deep neural networks ( DNNs ) with mini-batches , by normalizing the distribution of the internal representation for each hidden layer . However , the effectiveness of BN would diminish with scenario of micro-batch ( e.g. , less than 10 samples in a mini-batch ) , since the estimated statistics in a mini-batch are not reliable with insufficient samples .
Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization . In this work , we show that a commonly used deep network , which uses convolution , batch normalization , reLU , max-pooling , and sub-sampling pipeline , possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights . We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold .
We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normalization , in order to compute the most informative image features for our task . Additionally , we propose a new attention mechanism derived from this original idea , where the attention model for the visual input is conditioned on the source text encoder representations .
The problem of approximating the widely used ReLU non-linearity is then considered . An half-wave Gaussian quantizer ( HWGQ ) is proposed for forward approximation and shown to have efficient implementation , by exploiting the statistics of of network activations and batch normalization operations . To overcome the problem of gradient mismatch , due to the use of different forward and backward approximations , several piece-wise backward approximators are then investigated .
Conditional Batch Normalization ( CBN ) has proved to be an effective tool for visual question answering . However , previous CBN approaches fuse the linguistic information into image features via a simple affine transformation , thus they have struggled on compositional reasoning and object counting in images .
In this work we propose a novel GAN architecture which is able to generate photo-realistic images with a specified aesthetic quality by conditioning on both semantic and aesthetic information . To condition the generator , we propose a modified conditional batch normalization layer . To condition the discriminator , we use a joint probabilistic model of semantics and aesthetics to estimate the compatibility between an image ( either real or generated ) and the conditioning variable .
Different from the IDE proposed in [ reference ] , we discard the last 1000 - dimensional classification layer and add two fully connected ( FC ) layers . The output of the first FC layer has 1024 dimensions named as " FC - 1024 " , followed by batch normalization [ reference ] , ReLU and Dropout [ reference ] .
In all experiments , we use the Adam optimization algorithm adam to optimize the parameters of the generative models and variational approximations . Batch normalization batchnorm is implemented for convolutional layers and we always use a batch size of . We primarily use the Theano theano framework with the Lasagne lasagne library , but the final experiments with face data were done using Tensorflow tensorflow .
Layer normalization ( LayerNorm ) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix . However , the computational overhead introduced by LayerNorm makes these improvements expensive and significantly slows the underlying network , e.g. RNN in particular .
While the new models completely depart from the original recurrent architecture , we decided to investigate how to make RNNs more efficient . In this work , we propose a new recurrent NMT architecture , called Simple Recurrent NMT , built on a class of fast and weakly-recurrent units that use layer normalization and multiple attentions . Our experiments on the WMT14 English-to-German and WMT16 English-Romanian benchmarks show that our model represents a valid alternative to LSTMs , as it can achieve better results at a significantly lower computational cost .
A natural compromise is to normalize features by local context , while also taking into account group level information . In this pa- per , we propose Local Context Normalization ( LCN ) : a normalization layer where every feature is normalized based on a window around it and the filters in its group . We propose an algorithmic solution to make LCN efficient for arbitrary window sizes , even if every point in the image has a unique window .
In this paper , we introduce a novel deep model called Deep Sparse Coding Network ( DeepSCNet ) for Image classification . This new model includes four types of layers : the Sparse-coding layer , the Pooling layer , the Normalization layer and the Map reduction layer . The Sparse-coding laying does the general linear coding work for local patch within the receptive field .
To tackle problems above , we generalize effective learning ability of multilayer perception and propose a hybrid recommendation method with multilayer perception which jointly learns deep representations of users and items from side information as well as rating matrix and interaction function between user and item latent factors . We also propose to add a data normalization layer after combining two vectors coming from different magnitudes . Extensive experiments on two real world datasets show that our model outperforms state-of-the-art algorithms .
A squeeze-and-excitation module is utilized after each inception module to learn the weights of attention mechanism . A feature normalization layer is also added before the softmax layer , and compared with different discriminant loss functions including contrastive loss , triplet loss and center loss . Experimental results on both CASIA HWDB offline handwritten Chinese character datasets and historical Chinese samples show that the proposed method achieves improved recognition performance .
This was shown to work well in the recurrent setting . In this paper we propose a unified view of normalization techniques , as forms of divisive normalization , which includes layer and batch normalization as special cases . Our second contribution is the finding that a small modification to these normalization schemes , in conjunction with a sparse regularizer on the activations , leads to significant benefits over standard normalization techniques .
Moreover , it enables users to generate various stylish descriptions by plugging in style-specific parameters to include new styles into the existing model . We achieve this capability via a novel layer normalization layer design , which we will refer to as the Domain Layer Norm ( DLN ) . Extensive experimental validation and user study on various stylish image description generation tasks are conducted to show the competitive advantages of the proposed model .
In this paper , we aim at understanding the behaviors of Transformers in the large-scale speech recognition setting , where we have used around 65,000 hours of training data . We investigated various aspects on scaling up Transformers , including model initialization , warmup training as well as different Layer Normalization strategies . In the streaming condition , we compared the widely used attention mask based future context lookahead approach to the Transformer-XL network .
To address this problem , a body symmetry and part-locality-guided direct nonparametric deep feature enhancement ( DNDFE ) method is proposed in this article . Based on the observation that the body symmetry and part locality are two important appearance properties inherited in the upright walking persons , the proposed method designs two nonparametric layers , namely , the body symmetry average pooling and local normalization layers , to construct a DNDFE module to well explore the body symmetry and part locality properties . The proposed DNDFE module could be directly embedded between the traditional deep feature learning module and similarity learning module to enhance the DL features so as to improve the person Re-ID performance .
Similarly to prior work , our method learns by applying differentiable warping to frames and comparing the result to adjacent ones , but it provides several improvements : We address occlusions geometrically and differentiably , directly using the depth maps as predicted during training . We introduce randomized layer normalization , a novel powerful regularizer , and we account for object motion relative to the scene . To the best of our knowledge , our work is the first to learn the camera intrinsic parameters , including lens distortion , from video in an unsupervised manner , thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale .
The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) . We also include the FastSent variant “ FastSent + AE ” and the SkipThought - LN version that uses layer normalization . We report results from models trained on supervised data in the third group , and also report some results of supervised methods trained directly on each task for comparison with transfer learning approaches .
[ reference ][ reference ] . We also apply layer normalization [ reference ] ) to our models . For all of the character - level language modelling experiments , we apply the same procedure , but only change the number of hidden units , mini - batch size and the initial learning rate .
subsection : Convolutional modules To perform local computation , we use modules of convolutions with ReLU non - linearities and layer normalization . A module of convolutions gets as input a tensor of shape [ sequence length , feature channels ] and returns a tensor of the same shape .
We use 5 different aspect ratios { , , 1 , 2 , 3 } for all layers except that we ignore { , 3 } for conv3_3 . Following SSD , we also use normalization layer on conv3_3 , conv4_3 , and conv5_3 and initialize the norm 40 . For Fast R - CNN part , we extract features from conv3_3 , conv5_3 , and conv8 for each region proposal and concatenate all the features to predict class scores and further adjust the proposals .
In this paper we present the analysis of different representation techniques of the character images for extracting shape based features using modified signature method . An adaptive normalization is proposed that retains the aspect ratio of the character which is subjected to the fact that preservation of aspect ratio enhances the recognition process . Signature is generated from direction angle of the pixels where the pixels were taken from boundary , difference and skeletonized image of the character .
In this paper , an improved algorithm for enhancement of fingerprint image is proposed on the basis of the image normalization and Gabor filter Firstly , the adaptive normalization based on block processing is suggested for improvement of fingerprint images . An input image is partitioned into sub-blocks with the size of K /spl times/ L at first and the region of interest ( ROl ) of the fingerprint image is acquired The parameters for the image normalization are adaptively determined according to the statistics of each block .
We propose a novel domain adaptation method for deep learning that combines adaptive batch normalization to produce a common feature-space between domains and label transfer with subspace alignment on deep features . The first step of our method automatically conditions the features from the source/target domain to have similar statistical distributions by normalizing the activations in each layer of our network using adaptive batch normalization . We then examine the clustering properties of the normalized features on a manifold to determine if the target features are well suited for the second of our algorithm , label-transfer .
Recently , unsupervised exemplar-based image-to-image translation , conditioned on a given exemplar without the paired data , has accomplished substantial advancements . In order to transfer the information from an exemplar to an input image , existing methods often use a normalization technique , e.g. , adaptive instance normalization , that controls the channel-wise statistics of an input activation map at a particular layer , such as the mean and the variance . Meanwhile , style transfer approaches similar task to image translation by nature , demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style .
Without sacrificing image structure details , it enables the arbitrary style-content transfer . Second , accelerated with Adaptive Instance Normalization block , our framework achieved real-time speed required in the clinical US scanning . Third , an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other .
However , prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations , which could become a performance bottleneck on edge devices with insufficient computational power . To address this problem , we propose an inference accelerator for content generation ( ACG-Engine ) aimed to support the fundamental operations of generative networks , including convolution layers , deconvolution layers , specifically instance normalization layer . We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness , so that it can be efficiently mapped to the classic 2D processing element array .
We propose two modifications to these architectures and systematically compare them to mel-filterbanks , on the Wall Street Journal dataset . The first modification is the addition of an instance normalization layer , which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks . The second one relates to the low-pass filter used in these approaches .
On the other hand , the shapes of the Stage 2 polynomial kernels have also been shown to oscillate after a certain threshold within the standard normalized input space of [ -1,1 ] . A simple adaptive data normalization approach is therefore proposed to confine the data to the threshold window where these kernels exhibit the sought after ideal shape characteristics , hence eliminate the possibility of any data point to be located outside the range where these oscillations are observed . The implementation of the adaptive data normalization approach accordingly leads to a more accurate calculation of similarity measures and improves the classification performance . When compared to the standard normalized input space , experimental results ( performed on the Stage 2 kernels ) demonstrate the effectiveness of the proposed adaptive data normalization approach , with an average accuracy improvement factor of 11.772 % , depending on the dataset and the polynomial function utilized . Finally , a new perspective is also introduced whereby the utilization of orthogonal polynomials is perceived as a way of transforming the input space to another vector space , of the same dimensionality as the input space , prior to the kernel calculation step .
Due to the lack of training data in Asian face , a lot of well-known datasets , such as MS-Celeb-1 M , VGG Face and CASIA-Web Face that mainly consist of Caucasians , are applied to verify in Asians directly , which results in poor performance . To address this issue from the perspective of domain adaptation , in this paper , we propose a conceptually simple but novel method named Adaptive Instance Batch Normalization ( Ada-IBN ) . We first develop the method that transferring the knowledge from source to target domain to alleviate the discrepancy while increases the generalization capacity of CNNs .
To cope with larger differences between the target line art image and reference color images , our architecture utilizes non-local similarity matching to determine the region correspondences between the target image and the reference images , which are used to transform the local color information from the references to the target . To ensure global color style consistency , we further incorporate Adaptive Instance Normalization ( AdaIN ) with the transformation parameters obtained from a style embedding vector that describes the global color style of the references , extracted by an embedder . The temporal constraint network takes the reference images and the target image together in chronological order , and learns the spatiotemporal features through 3D convolution to ensure the temporal consistency of the target image and the reference image .
In this paper , we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time . At the heart of our method is a novel adaptive instance normalization ( AdaIN ) layer that aligns the mean and variance of the content features with those of the style features . Our method achieves speed comparable to the fastest existing approach , without the restriction to a pre-defined set of styles .
Unlike previous attention-based method which can not handle the geometric changes between domains , our model can translate both images requiring holistic changes and images requiring large shape changes . Moreover , our new AdaLIN ( Adaptive Layer-Instance Normalization ) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending on datasets . Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters .
An image representation is assumed to comprise both content information which is shared across domains and style information specific to one domain . By applying exemplar-based adaptive instance normalization to the shared content representation , EG-UNIT manages to transfer the style information in the target domain to the source domain . Experimental results on various datasets show that EG-UNIT can indeed translate the source image to diverse instances in the target domain with semantic consistency .
It is shown that , although the four flux normalization methods S(max ) , S(median ) , S(mean ) and S(unit ) belong to the same limit efficiency type , on the spectra with usual observing scale , S(max ) and S(median ) are much more efficient than S(mean ) and S(unit ) , and S(unit ) is the most inefficient one . This work is helpful for choosing the appropriate flux normalization method based on the size of spectra database and the scientific needs in automatic spectra analysis .
GN 's computation is independent of batch sizes , and its accuracy is stable in a wide range of batch sizes . On ResNet-50 trained in ImageNet , GN has 10.6 % lower error than its BN counterpart when using a batch size of 2 ; when using typical batch sizes , GN is comparably good with BN and outperforms other normalization variants . Moreover , GN can be naturally transferred from pre-training to fine-tuning .
On ResNet50 trained in ImageNet , KN has 3.4 % lower error than its BN counterpart when using a batch size of 4 ; Even when using typical batch sizes , KN still maintains an advantage over BN while other BN variants suffer a performance degradation . Moreover , KN can be naturally generalized to many existing normalization variants to obtain gains , e.g. equipping Group Normalization with Group Kalman Normalization ( GKN ) . KN can outperform BN and its variants for large scale object detection and segmentation task in COCO 2017 .
ScDNA-seq data is , however highly sparse and noisy due to the biases and artifacts that are introduced during the library preparation and sequencing procedure . Here , we propose SCOPE , a normalization and copy number estimation method for scDNA-seq data . The main features of SCOPE include : ( i ) a Poisson latent factor model for normalization , which borrows information across cells and regions to estimate bias , using negative control cells identified by cell-specific Gini coefficients ; ( ii ) modeling of GC content bias using an expectation-maximization algorithm embedded in the normalization step , which accounts for the aberrant copy number changes that deviate from the null distributions ; and ( iii ) a cross-sample segmentation procedure to identify breakpoints that are shared across cells from the same subclone .
Whole-genome single-cell DNA sequencing ( scDNA-seq ) enables characterization of copy-number profiles at the cellular level . We propose SCOPE , a normalization and copy-number estimation method for the noisy scDNA-seq data . SCOPE 's main features include the following : ( 1 ) a Poisson latent factor model for normalization , which borrows information across cells and regions to estimate bias , using in silico identified negative control cells ; ( 2 ) an expectation-maximization algorithm embedded in the normalization step , which accounts for the aberrant copy-number changes and allows direct ploidy estimation without the need for post hoc adjustment ; and ( 3 ) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background .
A hidden Markov model segments the word in a way that optimizes the global word score , using a dictionary in the process . A geometrical normalization scheme and a fast but efficient dictionary search are also presented . Trained on 20 k words from 59 writers , using a 25 k-word dictionary , our system reached recognition rates of 89 % for characters and 80 % for words on test data from a disjoint set of writers .
The invention discloses a correlation-quality sequencing image retrieval method based on tag retrieval . The correlation-quality sequencing image retrieval method comprises the following steps of : firstly , automatically sequencing social images according to the correlation of images and tags , fusing the vision consistency among the images and the vision consistency of the semanteme correlation among the images and the tags by utilizing a normalization frame , and solving an optimization problem by utilizing an iteration algorithm so as to obtain a correlation sequence ; and selecting the brightness , the contrast ratio and the color variety of the images to be used as quality characteristics of the images to balance previously returned images , overlaying correlation scores and quality scores by utilizing a linear model , and re-sequencing the images according to a final total score , and therefore , a correlation-quality sequencing algorithm is realized .
There are some internal and external sources increasing the influence of the phase noise . This paper investigates these sources , such as coupling between transmit and receive paths through the radar components , effects of targets having large RCS and surfaces of the radar platform in the radar operation zone generating unwanted echoes and proposes methods like RPC , DLC , windowing and video normalization to reduce the effect of this problem . Experimental results are also presented in this paper .
The invention relates to a fish identification method and system based on wavelet packet multi-scale information entropy . The method , based on an active sonar identification strategy , comprises step 101 ) : sending a sound signal to an underwater fish body and obtaining an echo signal backscattered from the underwater fish body ; step 102 ) : performing a filtering and an amplitude normalization preprocessing on the collected echo data of the fish body ; step 103 ) : extracting features of the preprocessed echo signal with a specific method being as follows : performing a wavelet packet multi-scale decomposition on the preprocessed echo signal to obtain a wavelet packet coefficient , and reconstituting the wavelet packet coefficient to obtain a time-domain signal in each frequency band of the fish body echo signal , and then extracting information entropy of the time-domain signal obtained through reconstituting the wavelet packet coefficient , wherein the information entropy constitutes a feature quantity ; and step 104 ) : inputting the feature quantity into a classifier to perform a classification treatment so that kind identification of the underwater fish body is completed .
This paper presents a shot boundary detection method based on the global decision tree that allows for extraction of boundaries of high variations occurring due to camera breaks from frame difference values . For a start , difference values between frames are calculated through local X2-histogram and normalization . Next , the distances between difference values are calculated through normalization .
This paper describes the development , implementation and validation of an automatic speaker recognition system on an iPad tablet . A score normalization approach , referred as Nearest Neighbor Normalization ( 3N ) , is applied in order to improve the baseline speaker verification system . The system is evaluated on the MOBIO corpus and results show an absolute improvement of the HTER by more than 4 % when the score normalization is performed .
The proposed approach was compared with three different approaches using the same neural network – one approach with no data augmentation , one with regularization not specific to visual data ( dropout and weight decay ) and one with extensive data augmentation specific for images . We conclude that by using the proposed set of augmentation techniques , including novel perturbed normalization , we postponed overfitting , which resulted in a better generalized model .
The results were illustrated in Figure [ reference ] . If one removed the batch normalization layers from the generator , the DCGAN would collapse without producing any face images . On the contrary , the LS - GAN still performed very well even if these batch normalization layers were removed , and there was no perceived deterioration or mode collapse of the generated images .
Fig . [ reference ] illustrates the basic building block of a Residual Network which consists of multiple convolutional and Batch Normalization layers . The identity transformation , is used to reduce the dimensions of to match those of .
Network architecture . Input images in each template are first passed through a convolutional neural network ( e.g. ResNet - 50 or SENet - 50 with an additional FC layer and L2 - normalization ) to produce a face descriptor per image . The descriptors are aggregated into a single fixed - length vector using the GhostVLAD layer .
The first term corresponds to the soft - assignment weight of the input vector x i for cluster k , while the second term computes the residual between the vector and the cluster centre . The final output is obtained by performing L2 normalization . GhostVLAD .
subsection : Gating In order to analyze whether a hard ( discrete ) choice through gating will outperform our normalization strategy , we experimented with using gates instead of the proposed concatenation - addition strategy . Specifically , we replaced the summation in Equation ( [ reference ] ) by a gating structure that sums up the contributions of the top branches with the highest probabilities .
Figure 9 shows the distribution of critic weight singular values , like Figure 2 , at more layers . Figure 11 and Table 2 show results for the spectral normalization variants considered in the experiments . MMDGAN , with neither spectral normalization nor a gradient penalty , did surprisingly well in this case , though it fails badly in other situations .
The most straightforward method of aggregating normalized scores is to compute their average . Without perfect score normalization , however , score averages tend to be heavily influenced by games such as Zaxxon for which baseline scores are high . Averaging inter - algorithm scores obviates this issue as all scores are bounded between 0 and 1 .
Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches Understanding symmetries in deep networks Modulating and attending the source image during encoding improves Multimodal Translation Deep Learning with Low Precision by Half-Wave Gaussian Quantization Conditional Kronecker Batch Normalization for Compositional Reasoning PFAGAN: An Aesthetics-Conditional GAN for Generating Photographic Fine Art Camera Style Adaptation for Person Re-identification Towards a Neural Statistician Root Mean Square Layer Normalization Deep Neural Machine Translation with Weakly-Recurrent Units Local Context Normalization: Revisiting Local Normalization Deep sparse coding network for image classification A Hybrid Recommendation Method with Multilayer Perception Applied on Real World Data Convolutional Neural Network with Attention Mechanism for Historical Chinese Character Recognition Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes Unsupervised Stylish Image Description Generation via Domain Layer Norm Exploring Transformers for Large-Scale Speech Recognition Body Symmetry and Part-Locality-Guided Direct Nonparametric Deep Feature Enhancement for Person Reidentification Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Hierarchical Multiscale Recurrent Neural Networks Depthwise Separable Convolutions for Neural Machine Translation Frustum PointNets for 3D Object Detection from RGB-D Data Analysis and Representation of Character Images for Extracting Shape based Features Towards Building an OCR for Bangla Script New enhancement algorithm for fingerprint images Manifold Guided Label Transfer for Deep Domain Adaptation Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer ACG-Engine: An Inference Accelerator for Content Generative Neural Networks End-to-End Speech Recognition From the Raw Waveform Investigation of orthogonal polynomial kernels as similarity functions for pattern classification by support vector machines Deep Domain Adaptation for Asian Face Recognition via Ada-IBN Deep Line Art Video Colorization with a Few References Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation Exemplar Guided Unsupervised Image-to-Image Translation [Efficient computation of spectral flux normalization]. Group Normalization Kalman Normalization: Normalizing Internal Representations Across Network Layers SCOPE: a normalization and copy number estimation method for single-cell DNA sequencing SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing. On-line cursive script recognition using time-delay neural networks and hidden Markov models Correlation-quality sequencing image retrieval method based on tag retrieval A guide to reduce the phase noise effect in FMCW Radars Fish identification method and system based on wavelet packet multi-scale information entropy Shot Boundary Detection Using a Global Decision Tree in Ubiquitous Environment Automatic speaker verification using nearest neighbor normalization (3N) on an iPad tablet Volumetrie Data Augmentation as an Effective Tool in MRI Classification Using 3D Convolutional Neural Network Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Deep Residual Networks with Exponential Linear Unit GhostVLAD for set-based face recognition Weighted Transformer Network for Machine Translation On gradient regularizers for MMD GANs The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract)
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