First , factors < inline-formula><tex-math notation="LaTeX">${{\rm{\lambda } } _ 1}$</tex-math></inline-formula > and < inline-formula><tex-math notation="LaTeX">${{\rm{\lambda } } _ 2}$</tex-math></inline-formula > are implemented to alter the linear constraint existing in the original GRU unit , enriching the information passing through . Then , a two-stream network structure is designed , equipped with some advanced network parameter adjustment techniques , such as batch normalization and dropout rate , to learn diverse features of the various process data . Finally , the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label . In a study that turned out to be surprisingly similar in spirit to Lillicrap et al. 's demonstration ( Lillicrap et al. 2014 ) but orthogonal in its results , our experiments indicate that : ( 1 ) the magnitudes of feedback weights do not matter to performance ( 2 ) the signs of feedback weights do matter — the more concordant signs between feedforward and their corresponding feedback connections , the better ( 3 ) with feedback weights having random magnitudes and 100 % concordant signs , we were able to achieve the same or even better performance than SGD . ( 4 ) some normalizations/stabilizations are indispensable for such asymmetric BP to work , namely Batch Normalization ( BN ) ( Ioffe and Szegedy 2015 ) and/or a " Batch Manhattan " ( BM ) update rule . Firstly , the HSV color space is used for spatial threshold segmentation , and traffic signs are effectively detected based on the shape features . Secondly , the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel , adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm . Finally , the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark . This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model . We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer , thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing . At the same time , we combine the output characteristics of each residual unit on the basis of the global residual structure , so the face feature information is more fully utilized and the model detail recovery ability is also improved . Since NC do n't need other normalization layer , NCNN looks like convolutional version of Self Normalizing Network.(SNN ) . Among micro-batch trainings , NCNN outperforms other batch-independent normalization methods . NCNN archives these superiority by standardizing rows of im2col matrix of inputs , which theoretically smooths the gradient of loss . Motivated by recent advances in deep domain adaptation , this paper introduces a deep architecture for estimating 3D keypoints when the training ( source ) and the test ( target ) images greatly differ in terms of visual appearance ( domain shift ) . Our approach operates by promoting domain distribution alignment in the feature space adopting batch normalization-based techniques . Furthermore , we propose to collect statistics about 3D keypoints positions of the source training data and to use this prior information to constrain predictions on the target domain introducing a loss derived from Multidimensional Scaling . The proposed design uses only on-chip memories . Furthermore , an efficient implementation of batch normalization operation is introduced . When evaluating the CIFAR-10 benchmark , the proposed FPGA design can achieve a processing rate of 332,158 images per second with with accuracy of 86.06 % using 1-bit quantized weights and activations . We demonstrate , using HFP8 , the successful training of deep learning models across a whole spectrum of applications including Image Classification , Object Detection , Language and Speech without accuracy degradation . Finally , we demonstrate that , by using the new 8 bit format , we can directly quantize a pre-trained model down to 8-bits without losing accuracy by simply fine-tuning batch normalization statistics . These novel techniques enable a new generations of 8-bit hardware that are robust for building and deploying neural network models . Implementation details . We fix the moving means and variations in batch normalization layers during fine - tuning . We use four devices on a single node . The output feature vector is produced by the final residual block ( block - 4 ) and averaged across the feature map locations . We use the original implementation of PointNet with all batch normalization layers removed . For the 2D object detector , we use an off - the - shelf Faster - RCNN implementation pretrained on MS - COCO and fine - tuned on the datasets used in the experiments . An up - sampling layer is added to the last convolution layer for re - scaling the output to the size of the input . We do not use any batch - normalization layers as we jointly train the discriminator with the segmentation network using a small batch size . Segmentation Network . In the case of MNIST with 100 labels , the test error after the first epochs is 16.50 % , after 50 epochs is 3.40 % , after 500 epochs is 2.21 % and after 5000 epochs is 1.80 % . Batch - normalization batch did not help in the case of the MNIST dataset . subsubsection : SVHN The last layer of the encoder has the softmax activation function . All the convolutional layers of the encoder except the softmax layer use batch - normalization batch . On the latent representation , we impose a categorical distribution with uniform probabilities . This subject remains an area of active inquiry , with various tweaks on ReLUs , e.g. PReLUs he2015prelu , and ELUs elu . Even with ReLUs , employing batch normalization batchnorm speeds training by reducing internal covariate shift . Good initialization can also ameliorate this problem glorot2010understanding , mishkin2015all . These feature maps are then convolved with a trainable decoder filter bank to produce dense feature maps . A batch normalization step is then applied to each of these maps . Note that the decoder corresponding to the first encoder ( closest to the input image ) produces a multi - channel feature map , although its encoder input has 3 channels ( RGB ) . As in the activation functions , i.e. PReLU and Batch normalization are employed as ’ pre - activation ’ steps . Thus , each convolutional block is composed by a batch normalization step , followed by a Parametric Rectified Linear Unit ( PReLU ) activation function and lastly the convolutional filters . The three fully - connected layers are composed of 400 , 200 and 150 hidden units , respectively . For generating the training targetsz , we need to correct for the startup bias in Z by dividing by factor ( 1 − α t ) . A similar bias correction has been used in , e.g. , [ reference ] and mean - only batch normalization [ reference ] . We observe that the recursion defined in Equation ( [ reference ] ) may lead to an exponential increase in the scale of feature values , especially when is large . In , the authors proposed the use of unshared batch normalization at every unfolding time to resolve this issue . Batch normalization is not used in our network ; we found that normalizing the scale with two scalar parameters was sufficient . The proposed network has an encoder-decoder architecture with skip connections and instance normalization . We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features , and demonstrate the effectiveness of instance normalization for image dehazing . Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets . A max-pooling layer selects one among a group of pixels and allows a similar interpretation by using selection masks . An instance-normalization layer subtracts a mean and divides by a standard deviation . If we fix the mean and standard deviation , it becomes linear . However , in most of existing methods , the normalization for each layer is fixed . Batch-Instance Normalization ( BIN ) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers . However , two potential issues exist in BIN : first , the Clip function is not differentiable at input values of 0 and 1 ; second , the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN . The main aim of this paper is to determine if we can create a colourisation model for paintings which generates artist-specific colourisations . Building on earlier work on natural-image colourisation , we propose a model capable of producing colourisations of paintings by incorporating a conditional normalisation scheme , i.e. , conditional instance normalisation . The results indicate that a conditional normalisation scheme is beneficial to the performance . In this paper , we propose a semi-supervised detector that adapts the domain shifts on both appearance and semantic levels . Based on this , two components are introduced as appearance adaptation networks with instance and batch normalization , and semantic adaptation networks where an adversarial transferring procedure is embedded by re-weighting the discriminator loss to improve the feature alignments between the two domains with imbalanced scales . Furthermore , a self-paced training procedure is performed to re-train the detector by alternately generating pseudo-labels in the target domain from easy to hard . That is , to learn a \reid { } model from a set of source domains that is suitable for application to unseen datasets out-of-the-box , without any model updating . Specifically , we observe that the domain discrepancy in \reid { } is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep \reid { } models . Instance Normalization~(IN ) in early layers filters out style statistic variations and Feature Normalization~(FN ) in deep layers is able to further eliminate disparity in content statistics . ILM ~ Norm provides a meta normalization mechanism and has several good properties . It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization , Layer Normalization , or Group Normalization . ILM ~ Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used . In this work , we propose a video multi-style transfer ( VMST ) framework which enables fast and multi-style video transfer within one single network . Specifically , we design a multi-instance normalization block ( MIN-Block ) to learn different style examples and two ConvLSTM modules to encourage the temporal consistency . The proposed algorithm is demonstrated to be able to generate temporally-consistent video transfer results in different styles while keeping each stylized frame visually pleasing . This differs from the usual protocol in that we apply dropout at test time , and we apply batch normalization using the statistics of the test batch , rather than aggregated statistics of the training batch . This approach to batch normalization , when the batch size is set to 1 , has been termed “ instance normalization ” and has been demonstrated to be effective at image generation tasks . In our experiments , we use batch sizes between 1 and 10 depending on the experiment . Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator . We leverage PatchGANs for the discriminator network , which classifies whether local image patches are real or fake . Parametric tests , such as two-sample t-tests , may be used to identify differentially expressed genes , but they require some assumptions that are not realistic for many practical problems . Non-parametric tests , such as empirical Bayes methods and mixture normal approaches , have been proposed , but the inferences are complicated and the tests may not have as much power as parametric models . RESULTS We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes . Chapter 4 extends the work of Chapter 3 to regression modelling , i.e. the quantification of cov-ariate effects on the CIF . A careful discussion of interpretational and identifiability issues which are intrinsic to models based on the mixture factorization is provided and the usage of the model is only recommended in settings with sufficient follow-up relative to the timing of the events . A simulation study demonstrates that the proposed approach is competitive compared to common statistical models for competing risks in terms of accuracy of parameter estimates and predictions . We show that similar observations are valid for 40 and 100-layers deep DenseNet architectures as well . We complement our study by evaluating the application of mixture normalization to the Generative Adversarial Networks ( GANs ) , where “ mode collapse ” hinders the training process . We solely replace a few batch normalization layers in the generator with our proposed mixture normalization . Our experiments using Deep Convolutional GAN ( DCGAN ) on CIFAR-10 show that mixture-normalized DCGAN not only provides an acceleration of < inline-formula><tex-math notation="LaTeX">$\sim 58\%$</tex-math><alternatives><mml : math><mml : mrow><mml : mo>∼</mml : mo><mml : mn>58</mml : mn><mml : mo>%</mml : mo></mml : mrow></mml : math><inline-graphic xlink : href="kalayeh-ieq2 - 2895781.gif"/></alternatives></inline-formula > but also reaches lower ( better ) “ Fréchet Inception Distance ” ( FID ) of 33.35 compared to 37.56 of its batch-normalized counterpart . These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples . ResultsWe propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic . The new method is evaluated using both simulated and real data . Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and , notably , failure to address batch-specific truncation of low abundance compounds . ResultsTo curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability , we propose mixture model normalization ( mixnorm ) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples . Mixnorm outperforms other approaches across many metrics , including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch . Thus , optical flow is used to extract velocities at each image frame , which are then reduced to speed and motion orientations . Using expectation maximization algorithm , we construct a mixture model of von Mises distribution describing the set of directions , and a mixture model of normal distribution related to the speed set . Each frame is compared with a collection of reference frames using distance of probability densities . Aim of this paper is to identify the behaviour of user 's in a time bound using an Expectation Maximization ( EM ) algorithm and the maximum likelihood estimates of the model parameters . A novel approach based on Mixture normal distribution is used to discuss the percentage of user along with web page frequency . Here we introduce One-Versus-Everyone Fold Change ( OVE-FC ) test for detecting SDEGs . To assess the statistical significance of such test , we also propose the scaled test statistic OVE-sFC together with a mixture null distribution model and a tailored permutation scheme . Validated with realistic synthetic data sets on both type 1 error and detection power , OVE-FC/sFC test applied to two benchmark gene expression data sets detects many known and de novo SDEGs . An example dataset , consisting of 9216 genes with four replicates per condition , resulted in four latent classes based on their similarity of the variance . ConclusionThe mixture variance model provides a realistic and flexible estimate for the variance of gene expression data under limited replicates . We believe that in using the latent class variances , estimated from a larger number of genes in each derived latent group , the p-values obtained are more robust than either using a constant gene or gene-specific variance estimate . Based on a probabilistic modeling approach , we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning . In technical terms , we model the input to a neural circuit using a normalized mixture model with Poisson noise . We demonstrate analytically and numerically that , in the presence of lateral inhibition introducing competition between different neurons , Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model . And further suppose that the target sequence that we observe ( ) is a noisy version of the unobserved true sequence : i.e. ( i ) , ( ii ) , where is , for example , a noise function that independently replaces each element in with a random element in with some small probability . In such a case , ideally the student ’s distribution should match the mixture distribution , In this setting , due to the noise assumption , now has significant probability mass around a neighborhood of ( not just at ) , and therefore the of the mixture distribution is likely something other than ( the observed sequence ) or ( the output from beam search ) . We can see that is a natural approximation to the of this mixture distribution between and for some . In this paper , we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs . Different from previous convolutional neural networks on graphs , we first design a motif-matching guided subgraph normalization method to capture neighborhood information . Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems . In this work , we present a novel motif-based attentional graph convolution neural network for graph classification , which can learn more discriminative and richer graph features . Specifically , a motif-matching guided subgraph normalization method is developed to better preserve the spatial information . A novel subgraph-level selfattention network is also proposed to capture the different impacts or weights of different subgraphs . Then we introduce a new kernel function that contains the effect of score normalization for SVM-based Speaker Verification system . We also show that these methods consistently improve the performance of the original system by means of implicit score normalization . In order to achieve more efficient computation , we evaluate an attempt to explore implicit score normalization in the much lower dimensional speaker factor space . Therefore , unlike M-BM system , there is no need to switch to different BMs depending on the claimant . Another advantage of the proposed method is that other additional normalization/compensation techniques can be easily applied since it is in a single UBM frame-work . The experiments are performed on NIST 2004 SRE core condition , and we show that the performance of the proposed method is close to the M-BM system with and without score normalization . The suggested approach consists of methods and techniques for generating data transformation rules needed for the data structure normalization . Keywords — Reverse Engineering , Database Integration , System Integration , Data Structure Normalization In this paper , we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer ’s clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models . We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model ( CDM ) v5 . We created a database schema design to store syntactic relations to facilitate efficient cohort queries . On the test set , fully automated AAMM performed well in 97 % of the cases ( average distance between manual and automatic landmark points was 3.3 mm , comparable to human interobserver variabilities ) . The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms . The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs . In the light of the affine transformation , several normalization methods are revisited . At the end of the paper , a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis . Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce . We consider two simple examples and treat them in detail : for the first one , arithmetic expressions , we construct an evaluation function ; for the second one , terms in the free monoid , we construct an accumulator-based flatten function . The resulting two functions are traditional reduction-free normalization functions . The construction builds on previous work on refocusing and on a functional correspondence between evaluators and abstract machines . In the first step , spectral subtraction filter is applied to speech spectrum . In the second step , we design a temporal structure normalization filter in order to apply to features extracted from the filtered spectrum . Our results on Aurora 2 show that the proposed method has higher recognition rate than both of spectral subtraction and temporal filtering methods . The segmentations were created manually by two clinical experts . Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA . Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient , target overlap , and false negative/positive value ( FNV/FPV ) measurements . In the case of SVHN with 1000 labels , the test error after the first epochs is 49.34 % , after 50 epochs is 25.86 % , after 500 epochs is 18.15 % and after 1000 epochs is 17.66 % . Batch - normalization were used in all the autoencoder layers including the softmax layer of , the linear layer of as well as the linear output layer of the autoencoder . We found batch - normalization batch to be crucial in training the AAE network on the SVHN dataset . This cycle will gradually enhance the accuracy in the target domain . section : Batch Normalization for Domain Adaptation Batch normalization ( BN ) [ reference ] , which whitens the output of the hidden layer in a CNN , is an effective technique to accelerate training speed and enhance the accuracy of the model . This makes our objective sensitive to the scaling of , even for a constant . We therefore normalize through either projection or batch - normalization with fixed scale . We refer to the model minimizing ( [ reference ] ) with as Counterfactual Regression ( CFR ) and the variant without balance regularization ( ) as Treatment - Agnostic Representation Network ( TARNet ) . Batch normalization can be applied to RNNs in different ways . In , the authors suggest to apply it to feed - forward connections only , while in the normalization step is extended to recurrent connections , using separate statistics for each time - step . In our work , we tried both approaches , but we did not observe substantial benefits when extending batch normalization to recurrent parameters ( i.e. , and ) . We propose a new mechanism , which we call Batch Normalization , that takes a step towards reducing internal covariate shift , and in doing so dramatically accelerates the training of deep neural nets . It accomplishes this via a normalization step that fixes the means and variances of layer inputs . Batch Normalization also has a beneficial effect on the gradient flow through the network , by reducing the dependence of gradients on the scale of the parameters or of their initial values . Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models . We discuss the importance of global as opposed to local normalization : a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models . section : Introduction We next describe how softmax - style normalization can be performed at the local or global level . subsection : Global vs. Local Normalization In the chen - manning:2014:EMNLP style of greedy neural network parsing , the conditional probability distribution over decisions given context is defined as where Each is a local normalization term . We utilize early stopping as a form of regularization in time when the model shows signs of overfitting on the validation set . We apply mean - variance normalization ( MVN ) after each ReLU activation layer to help improve link prediction performance , where it compensates for noise between train and test instances by normalizing the activations to have zero mean and unit variance . MVN enables efficient learning and has been shown effective in cardiac semantic segmentation and speech recognition . Bell proposed to use L2 normalization to each RoI - pooled feature and re - scale back up by a empirical scale , which shows a great gain on VOC dataset . In this paper , we also explore two different normalization ways to normalize the output of local and global FCN : an L2 normalization layer or a 1x1 convolutional layer to learn the scale . As shown in Table [ reference ] , we find that the use of L2 normalization decreases the performance greatly , even worse than the direct addition ( without any normalization ways ) . Hence , the convergence condition is satisfied . The above pre - normalization of covariance matrix nontrivially changes the data magnitudes such that it produces adverse effect on network . Hence , to counteract this change , after the Newton - Schulz iteration , we accordingly perform post - compensation , i.e. , An alternative scheme to counterbalance the influence incurred by pre - normalization is Batch Normalization ( BN ) . We applied this normalization after applying the ReLU nonlinearity in certain layers ( see Section 3.5 ) . 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 . Note that at this time , the size of is possibly different to . subsection : Graph Normalization The receptive field for a node is constructed by normalizing the neighborhood assembled in the previous step .
First , factors < inline-formula><tex-math notation="LaTeX">${{\rm{\lambda } } _ 1}$</tex-math></inline-formula > and < inline-formula><tex-math notation="LaTeX">${{\rm{\lambda } } _ 2}$</tex-math></inline-formula > are implemented to alter the linear constraint existing in the original GRU unit , enriching the information passing through . Then , a two-stream network structure is designed , equipped with some advanced network parameter adjustment techniques , such as batch normalization and dropout rate , to learn diverse features of the various process data . Finally , the learned features from the two streams are fused and a supervised learning regression layer is employed to decrease the error between the output and label .
In a study that turned out to be surprisingly similar in spirit to Lillicrap et al. 's demonstration ( Lillicrap et al. 2014 ) but orthogonal in its results , our experiments indicate that : ( 1 ) the magnitudes of feedback weights do not matter to performance ( 2 ) the signs of feedback weights do matter — the more concordant signs between feedforward and their corresponding feedback connections , the better ( 3 ) with feedback weights having random magnitudes and 100 % concordant signs , we were able to achieve the same or even better performance than SGD . ( 4 ) some normalizations/stabilizations are indispensable for such asymmetric BP to work , namely Batch Normalization ( BN ) ( Ioffe and Szegedy 2015 ) and/or a " Batch Manhattan " ( BM ) update rule .
Firstly , the HSV color space is used for spatial threshold segmentation , and traffic signs are effectively detected based on the shape features . Secondly , the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel , adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm . Finally , the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark .
This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model . We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer , thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing . At the same time , we combine the output characteristics of each residual unit on the basis of the global residual structure , so the face feature information is more fully utilized and the model detail recovery ability is also improved .
Since NC do n't need other normalization layer , NCNN looks like convolutional version of Self Normalizing Network.(SNN ) . Among micro-batch trainings , NCNN outperforms other batch-independent normalization methods . NCNN archives these superiority by standardizing rows of im2col matrix of inputs , which theoretically smooths the gradient of loss .
Motivated by recent advances in deep domain adaptation , this paper introduces a deep architecture for estimating 3D keypoints when the training ( source ) and the test ( target ) images greatly differ in terms of visual appearance ( domain shift ) . Our approach operates by promoting domain distribution alignment in the feature space adopting batch normalization-based techniques . Furthermore , we propose to collect statistics about 3D keypoints positions of the source training data and to use this prior information to constrain predictions on the target domain introducing a loss derived from Multidimensional Scaling .
The proposed design uses only on-chip memories . Furthermore , an efficient implementation of batch normalization operation is introduced . When evaluating the CIFAR-10 benchmark , the proposed FPGA design can achieve a processing rate of 332,158 images per second with with accuracy of 86.06 % using 1-bit quantized weights and activations .
We demonstrate , using HFP8 , the successful training of deep learning models across a whole spectrum of applications including Image Classification , Object Detection , Language and Speech without accuracy degradation . Finally , we demonstrate that , by using the new 8 bit format , we can directly quantize a pre-trained model down to 8-bits without losing accuracy by simply fine-tuning batch normalization statistics . These novel techniques enable a new generations of 8-bit hardware that are robust for building and deploying neural network models .
Implementation details . We fix the moving means and variations in batch normalization layers during fine - tuning . We use four devices on a single node .
The output feature vector is produced by the final residual block ( block - 4 ) and averaged across the feature map locations . We use the original implementation of PointNet with all batch normalization layers removed . For the 2D object detector , we use an off - the - shelf Faster - RCNN implementation pretrained on MS - COCO and fine - tuned on the datasets used in the experiments .
An up - sampling layer is added to the last convolution layer for re - scaling the output to the size of the input . We do not use any batch - normalization layers as we jointly train the discriminator with the segmentation network using a small batch size . Segmentation Network .
In the case of MNIST with 100 labels , the test error after the first epochs is 16.50 % , after 50 epochs is 3.40 % , after 500 epochs is 2.21 % and after 5000 epochs is 1.80 % . Batch - normalization batch did not help in the case of the MNIST dataset . subsubsection : SVHN
The last layer of the encoder has the softmax activation function . All the convolutional layers of the encoder except the softmax layer use batch - normalization batch . On the latent representation , we impose a categorical distribution with uniform probabilities .
This subject remains an area of active inquiry , with various tweaks on ReLUs , e.g. PReLUs he2015prelu , and ELUs elu . Even with ReLUs , employing batch normalization batchnorm speeds training by reducing internal covariate shift . Good initialization can also ameliorate this problem glorot2010understanding , mishkin2015all .
These feature maps are then convolved with a trainable decoder filter bank to produce dense feature maps . A batch normalization step is then applied to each of these maps . Note that the decoder corresponding to the first encoder ( closest to the input image ) produces a multi - channel feature map , although its encoder input has 3 channels ( RGB ) .
As in the activation functions , i.e. PReLU and Batch normalization are employed as ’ pre - activation ’ steps . Thus , each convolutional block is composed by a batch normalization step , followed by a Parametric Rectified Linear Unit ( PReLU ) activation function and lastly the convolutional filters . The three fully - connected layers are composed of 400 , 200 and 150 hidden units , respectively .
For generating the training targetsz , we need to correct for the startup bias in Z by dividing by factor ( 1 − α t ) . A similar bias correction has been used in , e.g. , [ reference ] and mean - only batch normalization [ reference ] .
We observe that the recursion defined in Equation ( [ reference ] ) may lead to an exponential increase in the scale of feature values , especially when is large . In , the authors proposed the use of unshared batch normalization at every unfolding time to resolve this issue . Batch normalization is not used in our network ; we found that normalizing the scale with two scalar parameters was sufficient .
The proposed network has an encoder-decoder architecture with skip connections and instance normalization . We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features , and demonstrate the effectiveness of instance normalization for image dehazing . Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets .
A max-pooling layer selects one among a group of pixels and allows a similar interpretation by using selection masks . An instance-normalization layer subtracts a mean and divides by a standard deviation . If we fix the mean and standard deviation , it becomes linear .
However , in most of existing methods , the normalization for each layer is fixed . Batch-Instance Normalization ( BIN ) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers . However , two potential issues exist in BIN : first , the Clip function is not differentiable at input values of 0 and 1 ; second , the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN .
The main aim of this paper is to determine if we can create a colourisation model for paintings which generates artist-specific colourisations . Building on earlier work on natural-image colourisation , we propose a model capable of producing colourisations of paintings by incorporating a conditional normalisation scheme , i.e. , conditional instance normalisation . The results indicate that a conditional normalisation scheme is beneficial to the performance .
In this paper , we propose a semi-supervised detector that adapts the domain shifts on both appearance and semantic levels . Based on this , two components are introduced as appearance adaptation networks with instance and batch normalization , and semantic adaptation networks where an adversarial transferring procedure is embedded by re-weighting the discriminator loss to improve the feature alignments between the two domains with imbalanced scales . Furthermore , a self-paced training procedure is performed to re-train the detector by alternately generating pseudo-labels in the target domain from easy to hard .
That is , to learn a \reid { } model from a set of source domains that is suitable for application to unseen datasets out-of-the-box , without any model updating . Specifically , we observe that the domain discrepancy in \reid { } is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep \reid { } models . Instance Normalization~(IN ) in early layers filters out style statistic variations and Feature Normalization~(FN ) in deep layers is able to further eliminate disparity in content statistics .
ILM ~ Norm provides a meta normalization mechanism and has several good properties . It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization , Layer Normalization , or Group Normalization . ILM ~ Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used .
In this work , we propose a video multi-style transfer ( VMST ) framework which enables fast and multi-style video transfer within one single network . Specifically , we design a multi-instance normalization block ( MIN-Block ) to learn different style examples and two ConvLSTM modules to encourage the temporal consistency . The proposed algorithm is demonstrated to be able to generate temporally-consistent video transfer results in different styles while keeping each stylized frame visually pleasing .
This differs from the usual protocol in that we apply dropout at test time , and we apply batch normalization using the statistics of the test batch , rather than aggregated statistics of the training batch . This approach to batch normalization , when the batch size is set to 1 , has been termed “ instance normalization ” and has been demonstrated to be effective at image generation tasks . In our experiments , we use batch sizes between 1 and 10 depending on the experiment .
Adapted from CycleGAN , StarGAN has the generator network composed of two convolutional layers with the stride size of two for downsampling , six residual blocks , and two transposed convolutional layers with the stride size of two for upsampling . We use instance normalization for the generator but no normalization for the discriminator . We leverage PatchGANs for the discriminator network , which classifies whether local image patches are real or fake .
Parametric tests , such as two-sample t-tests , may be used to identify differentially expressed genes , but they require some assumptions that are not realistic for many practical problems . Non-parametric tests , such as empirical Bayes methods and mixture normal approaches , have been proposed , but the inferences are complicated and the tests may not have as much power as parametric models . RESULTS We propose a weakly parametric method to model the distributions of summary statistics that are used to detect differentially expressed genes .
Chapter 4 extends the work of Chapter 3 to regression modelling , i.e. the quantification of cov-ariate effects on the CIF . A careful discussion of interpretational and identifiability issues which are intrinsic to models based on the mixture factorization is provided and the usage of the model is only recommended in settings with sufficient follow-up relative to the timing of the events . A simulation study demonstrates that the proposed approach is competitive compared to common statistical models for competing risks in terms of accuracy of parameter estimates and predictions .
We show that similar observations are valid for 40 and 100-layers deep DenseNet architectures as well . We complement our study by evaluating the application of mixture normalization to the Generative Adversarial Networks ( GANs ) , where “ mode collapse ” hinders the training process . We solely replace a few batch normalization layers in the generator with our proposed mixture normalization . Our experiments using Deep Convolutional GAN ( DCGAN ) on CIFAR-10 show that mixture-normalized DCGAN not only provides an acceleration of < inline-formula><tex-math notation="LaTeX">$\sim 58\%$</tex-math><alternatives><mml : math><mml : mrow><mml : mo>∼</mml : mo><mml : mn>58</mml : mn><mml : mo>%</mml : mo></mml : mrow></mml : math><inline-graphic xlink : href="kalayeh-ieq2 - 2895781.gif"/></alternatives></inline-formula > but also reaches lower ( better ) “ Fréchet Inception Distance ” ( FID ) of 33.35 compared to 37.56 of its batch-normalized counterpart .
These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples . ResultsWe propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic . The new method is evaluated using both simulated and real data .
Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and , notably , failure to address batch-specific truncation of low abundance compounds . ResultsTo curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability , we propose mixture model normalization ( mixnorm ) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples . Mixnorm outperforms other approaches across many metrics , including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch .
Thus , optical flow is used to extract velocities at each image frame , which are then reduced to speed and motion orientations . Using expectation maximization algorithm , we construct a mixture model of von Mises distribution describing the set of directions , and a mixture model of normal distribution related to the speed set . Each frame is compared with a collection of reference frames using distance of probability densities .
Aim of this paper is to identify the behaviour of user 's in a time bound using an Expectation Maximization ( EM ) algorithm and the maximum likelihood estimates of the model parameters . A novel approach based on Mixture normal distribution is used to discuss the percentage of user along with web page frequency .
Here we introduce One-Versus-Everyone Fold Change ( OVE-FC ) test for detecting SDEGs . To assess the statistical significance of such test , we also propose the scaled test statistic OVE-sFC together with a mixture null distribution model and a tailored permutation scheme . Validated with realistic synthetic data sets on both type 1 error and detection power , OVE-FC/sFC test applied to two benchmark gene expression data sets detects many known and de novo SDEGs .
An example dataset , consisting of 9216 genes with four replicates per condition , resulted in four latent classes based on their similarity of the variance . ConclusionThe mixture variance model provides a realistic and flexible estimate for the variance of gene expression data under limited replicates . We believe that in using the latent class variances , estimated from a larger number of genes in each derived latent group , the p-values obtained are more robust than either using a constant gene or gene-specific variance estimate .
Based on a probabilistic modeling approach , we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning . In technical terms , we model the input to a neural circuit using a normalized mixture model with Poisson noise . We demonstrate analytically and numerically that , in the presence of lateral inhibition introducing competition between different neurons , Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model .
And further suppose that the target sequence that we observe ( ) is a noisy version of the unobserved true sequence : i.e. ( i ) , ( ii ) , where is , for example , a noise function that independently replaces each element in with a random element in with some small probability . In such a case , ideally the student ’s distribution should match the mixture distribution , In this setting , due to the noise assumption , now has significant probability mass around a neighborhood of ( not just at ) , and therefore the of the mixture distribution is likely something other than ( the observed sequence ) or ( the output from beam search ) . We can see that is a natural approximation to the of this mixture distribution between and for some .
In this paper , we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs . Different from previous convolutional neural networks on graphs , we first design a motif-matching guided subgraph normalization method to capture neighborhood information . Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems .
In this work , we present a novel motif-based attentional graph convolution neural network for graph classification , which can learn more discriminative and richer graph features . Specifically , a motif-matching guided subgraph normalization method is developed to better preserve the spatial information . A novel subgraph-level selfattention network is also proposed to capture the different impacts or weights of different subgraphs .
Then we introduce a new kernel function that contains the effect of score normalization for SVM-based Speaker Verification system . We also show that these methods consistently improve the performance of the original system by means of implicit score normalization . In order to achieve more efficient computation , we evaluate an attempt to explore implicit score normalization in the much lower dimensional speaker factor space .
Therefore , unlike M-BM system , there is no need to switch to different BMs depending on the claimant . Another advantage of the proposed method is that other additional normalization/compensation techniques can be easily applied since it is in a single UBM frame-work . The experiments are performed on NIST 2004 SRE core condition , and we show that the performance of the proposed method is close to the M-BM system with and without score normalization .
The suggested approach consists of methods and techniques for generating data transformation rules needed for the data structure normalization . Keywords — Reverse Engineering , Database Integration , System Integration , Data Structure Normalization
In this paper , we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer ’s clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models . We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model ( CDM ) v5 . We created a database schema design to store syntactic relations to facilitate efficient cohort queries .
On the test set , fully automated AAMM performed well in 97 % of the cases ( average distance between manual and automatic landmark points was 3.3 mm , comparable to human interobserver variabilities ) . The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms . The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs .
In the light of the affine transformation , several normalization methods are revisited . At the end of the paper , a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis . Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce .
We consider two simple examples and treat them in detail : for the first one , arithmetic expressions , we construct an evaluation function ; for the second one , terms in the free monoid , we construct an accumulator-based flatten function . The resulting two functions are traditional reduction-free normalization functions . The construction builds on previous work on refocusing and on a functional correspondence between evaluators and abstract machines .
In the first step , spectral subtraction filter is applied to speech spectrum . In the second step , we design a temporal structure normalization filter in order to apply to features extracted from the filtered spectrum . Our results on Aurora 2 show that the proposed method has higher recognition rate than both of spectral subtraction and temporal filtering methods .
The segmentations were created manually by two clinical experts . Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA . Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient , target overlap , and false negative/positive value ( FNV/FPV ) measurements .
In the case of SVHN with 1000 labels , the test error after the first epochs is 49.34 % , after 50 epochs is 25.86 % , after 500 epochs is 18.15 % and after 1000 epochs is 17.66 % . Batch - normalization were used in all the autoencoder layers including the softmax layer of , the linear layer of as well as the linear output layer of the autoencoder . We found batch - normalization batch to be crucial in training the AAE network on the SVHN dataset .
This cycle will gradually enhance the accuracy in the target domain . section : Batch Normalization for Domain Adaptation Batch normalization ( BN ) [ reference ] , which whitens the output of the hidden layer in a CNN , is an effective technique to accelerate training speed and enhance the accuracy of the model .
This makes our objective sensitive to the scaling of , even for a constant . We therefore normalize through either projection or batch - normalization with fixed scale . We refer to the model minimizing ( [ reference ] ) with as Counterfactual Regression ( CFR ) and the variant without balance regularization ( ) as Treatment - Agnostic Representation Network ( TARNet ) .
Batch normalization can be applied to RNNs in different ways . In , the authors suggest to apply it to feed - forward connections only , while in the normalization step is extended to recurrent connections , using separate statistics for each time - step . In our work , we tried both approaches , but we did not observe substantial benefits when extending batch normalization to recurrent parameters ( i.e. , and ) .
We propose a new mechanism , which we call Batch Normalization , that takes a step towards reducing internal covariate shift , and in doing so dramatically accelerates the training of deep neural nets . It accomplishes this via a normalization step that fixes the means and variances of layer inputs . Batch Normalization also has a beneficial effect on the gradient flow through the network , by reducing the dependence of gradients on the scale of the parameters or of their initial values .
Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models . We discuss the importance of global as opposed to local normalization : a key insight is that the label bias problem implies that globally normalized models can be strictly more expressive than locally normalized models . section : Introduction
We next describe how softmax - style normalization can be performed at the local or global level . subsection : Global vs. Local Normalization In the chen - manning:2014:EMNLP style of greedy neural network parsing , the conditional probability distribution over decisions given context is defined as where Each is a local normalization term .
We utilize early stopping as a form of regularization in time when the model shows signs of overfitting on the validation set . We apply mean - variance normalization ( MVN ) after each ReLU activation layer to help improve link prediction performance , where it compensates for noise between train and test instances by normalizing the activations to have zero mean and unit variance . MVN enables efficient learning and has been shown effective in cardiac semantic segmentation and speech recognition .
Bell proposed to use L2 normalization to each RoI - pooled feature and re - scale back up by a empirical scale , which shows a great gain on VOC dataset . In this paper , we also explore two different normalization ways to normalize the output of local and global FCN : an L2 normalization layer or a 1x1 convolutional layer to learn the scale . As shown in Table [ reference ] , we find that the use of L2 normalization decreases the performance greatly , even worse than the direct addition ( without any normalization ways ) .
Hence , the convergence condition is satisfied . The above pre - normalization of covariance matrix nontrivially changes the data magnitudes such that it produces adverse effect on network . Hence , to counteract this change , after the Newton - Schulz iteration , we accordingly perform post - compensation , i.e. , An alternative scheme to counterbalance the influence incurred by pre - normalization is Batch Normalization ( BN ) .
We applied this normalization after applying the ReLU nonlinearity in certain layers ( see Section 3.5 ) . 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 .
Note that at this time , the size of is possibly different to . subsection : Graph Normalization The receptive field for a node is constructed by normalizing the neighborhood assembled in the previous step .
Data-Driven Modeling Based on Two-Stream ${\rm{\lambda }}$ Gated Recurrent Unit Network With Soft Sensor Application How Important Is Weight Symmetry in Backpropagation? Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles The visual human face super-resolution reconstruction algorithm based on improved deep residual network Normalized Convolutional Neural Network Structured Domain Adaptation for 3D Keypoint Estimation Deep learning binary neural network on an FPGA Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks Wider or Deeper: Revisiting the ResNet Model for Visual Recognition PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Learning to Adapt Structured Output Space for Semantic Segmentation Adversarial Autoencoders PixelGAN Autoencoders FractalNet: Ultra-Deep Neural Networks without Residuals SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Temporal Ensembling for Semi-Supervised Learning Image Super-Resolution via Dual-State Recurrent Networks Strong Baseline for Single Image Dehazing with Deep Features and Instance Normalization Understanding Convolutional Networks Using Linear Interpreters - Extended Abstract U-Net Training with Instance-Layer Normalization A Learned Representation of Artist-Specific Colourisation Object detection based on semi-supervised domain adaptation for imbalanced domain resources Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice Instance-Level Meta Normalization Fast Video Multi-Style Transfer Image-to-Image Translation with Conditional Adversarial Networks StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation A spline function approach for detecting differentially expressed genes in microarray data analysis Flexible models for competing risks and weighted analyses of composite endpoints Training Faster by Separating Modes of Variation in Batch-Normalized Models An adaptive method for cDNA microarray normalization Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data Abnormal Crowd Behavior Detection Using Speed and Direction Models Optimizing the Behaviour of Web Users Through Expectation Maximization Algorithm and Mixture of Normal Distributions Data-driven robust detection of tissue/cell-specific markers A full Bayesian hierarchical mixture model for the variance of gene differential expression Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin? Sequence-Level Knowledge Distillation Graph Convolutional Neural Networks via Motif-based Attention Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification Exploring implicit score normalization techniques in speaker verification Use of VTL-wise models in feature-mapping framework to achieve performance of multiple-background models in speaker verification Using Automated Database Reverse Engineering for Database Integration An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria Automatic segmentation of echocardiographic sequences by active appearance motion models Low-level analysis of microarray data From Reduction-based to Reduction-free Normalization Robust speech recognition using spectral subtraction and temporal structure normalization Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus Adversarial Autoencoders Asymmetric Tri-training for Unsupervised Domain Adaptation Estimating individual treatment effect: generalization bounds and algorithms Light Gated Recurrent Units for Speech Recognition Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Globally Normalized Transition-Based Neural Networks Learning to Make Predictions on Graphs with Autoencoders CoupleNet: Coupling Global Structure with Local Parts for Object Detection Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization ImageNet classification with deep convolutional neural networks Learning Convolutional Neural Networks for Graphs
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