In general , very long time-series ( months to years ) as well as massive normalization and/or data selection are necessary to obtain useful cross-correlation functions . One task of this study is to evaluate the influence of different established normalization methods on the obtained cross-correlation functions . Furthermore , we evaluate two waveform preserving time domain normalizations as well as a new fully automated data selection approach . We compare extended batch normalization with batch normalization and group normalization on the datasets of MNIST , CIFAR-10/100 , STL-10 , and ImageNet , respectively . The experiments show that extended batch normalization alleviates the problem of batch normalization with small batch size while achieving close performances to batch normalization with large batch size . Another fundamental problem with BN is that it can not be used with batch-size $ 1 $ during training . We address these drawbacks of BN by proposing a non-adaptive normalization technique for removing covariate shift , that we call \textit{Normalization Propagation}. Our approach does not depend on batch statistics , but rather uses a data-independent parametric estimate of mean and standard-deviation in every layer thus being computationally faster compared with BN . We exploit the observation that the pre-activation before Rectified Linear Units follow Gaussian distribution in deep networks , and that once the first and second order statistics of any given dataset are normalized , we can forward propagate this normalization without the need for recalculating the approximate statistics for hidden layers . BackgroundAnalysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other . Both simple total array intensity-based as well as more complex " local intensity level " dependent normalization methods have been developed , some of which are widely used . Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors . We demonstrate the effectiveness of the proposed approach with experiments using the widely used SITW and CNCeleb corpora . In these experiments , the DNF-based normalization delivered substantial performance gains and also showed strong generalization capability in out-of-domain tests . Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging ( LiDAR ) point clouds acquired by mobile mapping systems ( MMS ) . This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction . For comparative evaluation , original intensity thresholding and deep learning using manually established labels strategies are also implemented . The original quaternion convolution operation is presented and constructed to fully mix the information of color channels . The quaternion batch normalization and pooling operations are derived and designed in quaternion domain to further ensure the integrity of color information . Meanwhile , the knowledge of the attention mechanism is incorporated to boost the performance of the proposed QCNN . Violations of economic rationality principles in choices between three or more options are critical for understanding the neural and cognitive mechanisms of decision-making . A recent study reported that the relative choice accuracy between two options decreases as the value of a third ( distractor ) option increases and attributed this effect to divisive normalization of neural value representations . In two preregistered experiments , a direct replication and an eye-tracking experiment , we assessed the replicability of this effect and tested an alternative account that assumes value-based attention to mediate the distractor effect . To fully leverage the Near-Infrared channel , the traditional Red-Green-Blue channels , and Vegetation Index or its variants , we propose a Generalized Vegetation Index ( GVI ) , a lightweight module that can be easily plugged into many neural network architectures to serve as an additional information input . To smoothly train models with our GVI , we developed an Additive Group Normalization ( AGN ) module that does not require extra parameters of the prescribed neural networks . Our approach has improved the IoUs of vegetation-related classes by 0.9 — 1.3 percent and consistently improves the overall mIoU by 2 percent on our baseline . We have considered various socioeconomic , psychological and academic factors to fully understand what a person 's life is during adolescence and how those factors impact their academic performance . Using pre-processing techniques such as feature selection , data balancing , discretization and normalization , and classification techniques such as Random Forest , Artificial Neural Net ( ANN ) , and Naïve Bayes we have developed different models to predict academic performance and also investigated prominent patterns in this study . “ Batch normalization ” also centers activations with the goal to counter the internal covariate shift . Also the Projected Natural Gradient Descent algorithm ( PRONG ) centers the activations by implicitly whitening them . Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first - order pooling . Recent works have shown matrix square root normalization plays a central role in achieving state - of - the - art performance . However , existing methods depend heavily on eigendecomposition ( EIG ) or singular value decomposition ( SVD ) , suffering from inefficient training due to limited support of EIG and SVD on GPU . B - CNN is one of the first end - to - end covariance pooling ConvNets . It performs element - wise square root normalization followed by normalization for covariance matrix , achieving impressive performance in FGVC task . Improved B - CNN shows that additional matrix square root normalization before element - wise square root and normalization can further attain large improvement . Implementation of iSQRT - COV Meta - layer We encapsulate our code in three computational blocks , which implement forward & backward computation of pre - normalization layer , Newton - Schulz iteration layer and post - compensation layer , respectively . The code is written in C ++ based on NVIDIA on top of CUDA toolkit 8.0 . However , the model is so numerically sensitive that we can not operate a stable training on it the original training settings . A possible reason is that , since the feature space of CNN changes rapidly during the training procedure , linear transformation without any normalization may not be able to incorporate the information contained in the feature vector leaked from D. Here we present more examples for illustrating the interaction of WORKER and MANAGER to support our claims in the main text as below . Corresponding expert segmentations were hidden and results had to be submitted to an online evaluation platform . Similar to BRATS , the only pre - processing that we applied is the normalization of each image to the zero - mean and unit variance . subsubsection : Experimental Setting In this way , the computation of becomes slightly more efficient than the one shown in Figure [ reference ] , for we save the fusion operation . Finally , an additional saving of the normalization step derives from using the stored in the backward implementation of BN rather than recomputing it from . To distinguish the efficient backward implementation of BN from the standard one we write in place of ( cyan - colored , see additionally § [ reference ] ) . We train on this objective by including multiple paragraphs from the same context in each mini - batch . This is similar to simply feeding the model multiple paragraphs from each context concatenated together , except that each paragraph is processed independently until the normalization step . The key idea is that this will force the model to produce scores that are comparable between paragraphs , even though it does not have access to information about the other paragraphs being considered . Tab . [ reference ] shows how each normalization step helps for 3D detection . We see that both frustum rotation ( such that frustum points have more similar XYZ distributions ) and mask centroid subtraction ( such that object points have smaller and more canonical XYZ ) are critical . However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size . Due to these optimization challenges in RNNs , the application of batch normalization and its variants ( layer normalization , recurrent batch normalization , recurrent normalization propagation ) have not been as successful as their counterparts in feed - forward networks , although they do considerably provide performance gains . Similarly , naive application of dropout has been shown to be ineffective in RNNs . Similarly as a substitute for batch normalization , layer normalization normalizes the hidden units within each sample to have zero mean and unit standard deviation . Recurrent batch normalization applies batch normalization but with unshared mini - batch statistics for each time step . and on the other hand show that activity regularization ( AR ) and temporal activation regularization ( TAR ) are also effective methods for regularizing LSTMs . It achieves test accuracy of 96.1 with only 44k parameters . Note that the previous state - of - the - art utilizes the recurrent batch normalization . The version without it has a much lower performance compared to all the dilated models . Normalization techniques As described in Section 4 , we use the exponential function followed by pixel - wise normalization across channels in several stages of the CRF - RNN . Since this operation has a tendency to result in small gradients with respect to the input when the input value is large , we conducted several experiments where we replaced this by a rectifier linear unit ( ReLU ) operation followed by a normalization across the channels . al showed that a the VGG - 16 very deep architecture , when trained with a broad and deep dataset containing one thousand examples of 2622 subjects , outperformed networks with specialized networks and ensembles on YouTubeFaces . Second , many top performing approaches use some form of pose normalization such as 2D / 3D alignment to warp the facial landmarks into a canonical frontal pose . Finally , many approaches use metric learning in the form of triplet loss similarity or joint Bayesian metric learning for the final loss to learn an optimal embedding for verification . We report average prediction accuracies and standard deviations . For Patchy - san ( referred to as PSCN ) , we used - dimensional WL normalization , a width equal to the average number of nodes ( see Table [ reference ] ) , and receptive field sizes of and . For the experiments we only used node attributes . In order not to use words in irrelevant passages , our model introduces the concept of combined attention SunHLLMT18 . While the original technique combines the word and sentence level attentions , our model combines the passage - level relevance and word - level attentions by using simple scalar multiplication and re - normalization . The updated word attention is : Recently , alternative methods for normalizing feature activations in neural networks have been proposed . Among them , group normalization has been shown to yield similar , in some domains even superior performance to batch normalization . All these methods utilize a learned affine transformation after the normalization operation to increase representational power . They observe that only a handful of clients ( enrolled users in the gallery ) actually contribute disproportionately to recognition errors . While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies , in this study , we explore a novel category of approaches : group-specific score normalization . While client-specific score normalization can be negatively impacted by the paucity of genuine score samples , group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated . The caller makes the call by dialing or orally giving the telephone number of the call destination or by inputting an abbreviated dial code relating to the telephone number . The equipment is provided with a speaker verification system adopting group normalization scoring . The group normalization scoring provides a dynamic threshold value to a verification process and further resistance force to fluctuation in training utterance and verification utterance is supplied to the process . The fluctuation is due to the fluctuation of the size of the voice of the speaker or communication channel characteristics . In particular , dilated convolution layers with symmetrically fixed rates are stacked to learn spatial attention . A channel attention mechanism with the cross-level group normalization and squeeze-and-excitation operation is proposed . Aligned attention is constructed with organized deformable filters . When designing the lightweight network , Concatenated Rectified Linear Units ( CReLU ) and depthwise separable convolution operation were employed to reduce the computation cost and model size . When training the lightweight network from scratch , we employ Group Normalization ( GN ) in each convolution layer , which makes smoother optimization landscape and has more stable gradients . A serial of ablation experiments is conducted on the recently published large-scale Dataset for Object detection in Aerial images ( DOTA ) , and the results show that the proposed object detection methods with lightweight network trained from scratch achieves competitive performance but has smaller model size and lower computation cost . A novel detection algorithm based on fully convolutional networks with fusion of multilayer convolutional features is proposed in this paper . This method predict directly the classification and bounding boxes of the objects in the aerial images based on pixel information , and improve the network framework by use of Group Normalizer and deformable convolution to promote accuracy of object detection . The experimental results on DOTA dataset with oriented bounding boxes show that the detection algorithm presented in this paper possesses fast detection speed and higher accuracy . According to the feature normalization method , only the diversity problem of data distribution among features can be solved , and the problem of the large distribution difference of feature internal data can not be solved . According to the grouping normalization method , the advantage of an overall feature normalization method is retained , meanwhile , the problem of the overlarge distribution scale in the feature data is solved , and the classification accuracy is improved . The feature grouping normalization method has strong robustness . This paper proposes a novel normalization group strategy ( NGS ) to extend brain storm optimization ( BSO ) for power electronic circuit ( PEC ) design and optimization . As different variables in different dimensions of the PEC represent different circuit components such as resistor , capacitor , or inductor , they have different physical significances and various search space that are even not in comparable range . Quantum Field Theory ( QFT ) [ 11 is the basis of the modern high energy physics [ 21 . The property of the asymptotic freedom in some QFT models allows us to use perturbation theory ( PT ) [ 1.1 , which in combination with the method of re-normalization group ( RG ) [ 1 ] is the main calculat ional tool of the QFT . Usually , the applied QFT deals with Feynman integrals ( FI ) . Synchronized batch - normalization slows down training significantly and training on GPUs is also impractical for most people . Therefore , group normalization has been recently proposed so that instance level recognition tasks can benefit from another form of normalization in a low batch setting during training . With SNIPER , we show that the image resolution bottleneck can be alleviated for instance level recognition tasks . The encoder part uses ResNet blocks , where each block consists of two convolutions with normalization and ReLU , followed by additive identity skip connection . For normalization , we use Group Normalization ( GN ) , which shows better than BatchNorm performance when batch size is small ( bath size of 1 in our case ) . We follow a common CNN approach to progressively downsize image dimensions by 2 and simultaneously increase feature size by 2 . The invention discloses a data control method based on data platforms . The data control method includes : ( 1 ) acquiring data of a plurality of the data platforms and integrating the data , wherein the integrated data includes user data of the data platforms , original data of data items , multi-dimensional descriptions of user behavior , multi-dimensional descriptions of the data items , online data and offline data ; ( 2 ) processing the integrated data in a distributed processing frame mode and performing normalization operation , standardization operation and data cleaning operation , wherein the normalization operation refers to performing normalization operation to numerical data , the standardization operation refers to organizing the data in structuralization , keeping the data integrity , reducing redundancy and increasing the uniformity of the data , and the data cleaning operation refers to perform the data cleaning operation to incomplete data , wrong data and repeated data ; and ( 3 ) extracting the processed data and displaying the data . The data control method based on the data platforms improves the speed of data searching through a novel data control mode . Biometric identification technology based on hand vein subcutaneous network structure appears as a promising technique for personal recognition . The contrast of hand vein image is enhanced by using Wiener filter , normalized mode , gray level stretching , sharpening and so on . After binary image was obtained , four-domain region growing algorithm was presented to differentiate and get rid of the noise areas . The manner in which floating-point arithmetic is handled in this mode is discussed . Several numerical experiments using this mode are described and comparisons are made with the ordinary “ normalized mode . ” Examples include power series evaluation , linear equations solution , determinant evaluation and matrix inversion . When only two spatially adjacent channels of the seven-channel system were active , overall performance was substantially better with a geometrically spaced spatial configuration ( with far-field talkers at −90 ° , −30 ° , −10 ° , 0 ° , + 10 ° , + 30 ° , and + 90 ° azimuth ) or a hybrid near-far configuration ( with far-field talkers at −90 ° , −30 ° , 0 ° , + 30 ° , and + 90 ° azimuth and near-field talkers at ±90 ° ) than with a more conventional linearly spaced configuration ( with far-field talkers at −90 ° , −60 ° , −30 ° , 0 ° , + 30 ° , + 60 ° , and + 90 ° azimuth ) . When all seven channels were active , performance was generally better with a “ better-ear ” normalization scheme that equalized the levels of the talkers in the more intense ear than with a default normalization scheme that equalized the levels of the talkers at the center of the head . The best overall performance in the seven-talker task occurred when the hybrid near-far spatial configuration was combined with the better-ear normalization scheme . Using case studies from recent single-cell RNA-seq , Nanostring and Metagenomics studies , we highlight its general utility and , in particular , show a ) the ability to correctly model single-cell RNA-seq data and do comparisons with 1/5th the amount of sequencing currently used and b ) that the selection of suitable statistical tests strongly impacts the ability to detect biomarkers in Metagenomic studies . Furthermore , we demonstrate that a novel mode-based normalization employed in EDDA uniformly improves in robustness over existing approaches ( 10 - 20 % ) and increases precision to detect differential abundance by up to 140 % . The invention relates to a steering arm shaft of an automobile power steering gear , in particular to a manufacturing method for a gear on the steering arm shaft of the automobile power steering gear . A forging die is included ; the method includes the following steps : metal blank is heated to 1,150DEG C to 1,200 DEG C and is put into a blank forming die of the forging die , and the volume of the blank is reasonably distributed by forging to obtain a forging bar of the steering arm shaft ; the forging bar of the steering arm shaft is taken out and reheated to 850 DEG C and 900 DEG C ; the heated forging bar of the steering arm shaft is taken out and put in the die impression of the forging die for molding and forging to obtain a molded steering arm shaft with gear teeth on the shaft body ; after forging , the gear teeth are reserved with cutting amount for machining ; a normalizing mode is adopted for the heat treatment of the molded steering arm shaft ; cutting is carried out to the cutting amount reserved on the gear teeth , and superficial treatment is carried out after cutting . The manufacturing method of the invention can reduce cutting amount , shorten cutting time , improve the strength of the gear and lower manufacturing cost . subsection : Coupling structure To match the same order of magnitude , we apply a normalization operation to the output of local and global FCN before they are combined together . We explored two different methods to perform normalization : an L2 normalization layer or a 1x1 convolutional layer to model the scale . In this paper , we explore the structure of neural loss functions , and the effect of loss landscapes on generalization , using a range of visualization methods . First , we introduce a simple " filter normalization " method that helps us visualize loss function curvature , and make meaningful side-by-side comparisons between loss functions . Then , using a variety of visualizations , we explore how network architecture affects the loss landscape , and how training parameters affect the shape of minimizers . We develop color normalization filters based on a total of 319 images which normalize color of images using the HSV color system . We determined that the color characteristics of the peripheral part of the tumors significantly influence the color normalization and confirmed that the developed normalization filter achieved satisfactory normalization performance as evaluated by a cross-validation test . This paper proposes a methodology of the pixel-based algorithm for forest change detection using PALSAR-2 ScanSAR data . Frost filter is applied to reduce speckle noise and normalization filter is implemented to reduce ground moisture effects . In case of Peru and Paraguay , the user ’s accuracy of developed algorithm was almost same as those of JJ-FAST version-2 while the producer ’s accuracy was 3 times better than JJ-FAST latest version ( version-2 ) . In addition , sequential thinning and thickening morphological method is used to construct the skeleton model . The proposed hybrid approach is compared with the normalized filter . As a result , the proposed approach produces better skeleton model with less influential effect on shadow and illumination . A study has been carried out using MODTRAN 4.0 radiative transfer code simulations to calculate the brightness temperatures expected at the Advanced Very High Resolution Radiometer ( AVHRR ) on board the National Oceanic and Atmospheric Administration NOAA satellites series ( 7 , 9 , 11 , 12 , 14 , 15 , 16 , 17 , 18 and 19 ) by using Gaussian Referential Filter ( GRF ) and Gaussian Filter ( GF ) instead of filter Normalized Filter obtained from NOAA agency . The outputs of applying MODTRAN 4.0 are values of atmospheric parameters obtained by mathematical convolution using GRF and GF Filters . A detailed analysis of the total error in LST , d Total(Ts ) , in function of AVHRR/NOAA satellites , shows that the algorithms are able to estimate accurate LST between a minimum of 1.256 K and a maximum of 1.415 K with amplitude of about 0.159 K. The validations show also that the algorithms are capable to produce LST with a standard deviation lower than 1.554 K and a Root Mean Square Error ( RMSE ) lower than 1.558 K. This result gives the opportunity to use the filter GF instead of filter Normalized Filter obtained from NOAA agency , in other studies by creation of GF filters centered in any region of the electromagnetic . This filter is applied on the logarithmic energy outputs of Mel spaced filter banks . We also demonstrate the effectiveness of the proposed channel normalizing filter in improving speaker verification performance in mismatched conditions . GMM-based systems often use thousands of mixture components and hence require a large number of parameters to characterize each target speaker . However , they either do not match the performance of BN for large batches , or still exhibit degradation in performance for smaller batches , or introduce artificial constraints on the model architecture . In this paper we propose the Filter Response Normalization ( FRN ) layer , a novel combination of a normalization and an activation function , that can be used as a replacement for other normalizations and activations . Our method operates on each activation channel of each batch element independently , eliminating the dependency on other batch elements . We formulate both learning and inference as constrained optimization problems and solve them using augmented Lagrange method . The experiment results demonstrate that the normalization of filters at different scales helps to achieve fast convergence in learning stage and obtain superior performance in image restoration , e.g. , image denoising and image inpainting . A normalized digital two-pair has unity L p - scaling norms at all pertinent internal nodes and the two output nodes . A simple technique is proposed to derive the normalized filter structure recently forwarded by Gray and Markel . Additional normalized digital two-pairs are also derived . In this paper , we propose a simple yet effective parametric adaptive filter ( PAF ) and demonstrate its excellent performance in acoustic echo cancellation applications . Specifically , the proposed PAF decomposes an adaptive filter into three components : a normalized finite impulse response filter , a gain factor and a time-shift factor . With a novel update scheme for the PAF , these three components can be adjusted with different step-size respectively . Most defending methods only focus on tuning the DNN itself , but we propose a novel defending method which modifies the input data to detect the adversarial examples . We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work . The framework gives the decision by comparing the classification results of original input and multiple normalized inputs . Deformable convolution is capable of learning receptive fields adaptively , as shown in Figure [ reference ] , [ reference ] and Table [ reference ] . Atrous convolution [ ] It increases a normal filter ’s stride to be larger than and keeps the original weights at sparsified sampling locations .
In general , very long time-series ( months to years ) as well as massive normalization and/or data selection are necessary to obtain useful cross-correlation functions . One task of this study is to evaluate the influence of different established normalization methods on the obtained cross-correlation functions . Furthermore , we evaluate two waveform preserving time domain normalizations as well as a new fully automated data selection approach .
We compare extended batch normalization with batch normalization and group normalization on the datasets of MNIST , CIFAR-10/100 , STL-10 , and ImageNet , respectively . The experiments show that extended batch normalization alleviates the problem of batch normalization with small batch size while achieving close performances to batch normalization with large batch size .
Another fundamental problem with BN is that it can not be used with batch-size $ 1 $ during training . We address these drawbacks of BN by proposing a non-adaptive normalization technique for removing covariate shift , that we call \textit{Normalization Propagation}. Our approach does not depend on batch statistics , but rather uses a data-independent parametric estimate of mean and standard-deviation in every layer thus being computationally faster compared with BN . We exploit the observation that the pre-activation before Rectified Linear Units follow Gaussian distribution in deep networks , and that once the first and second order statistics of any given dataset are normalized , we can forward propagate this normalization without the need for recalculating the approximate statistics for hidden layers .
BackgroundAnalysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other . Both simple total array intensity-based as well as more complex " local intensity level " dependent normalization methods have been developed , some of which are widely used . Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors .
We demonstrate the effectiveness of the proposed approach with experiments using the widely used SITW and CNCeleb corpora . In these experiments , the DNF-based normalization delivered substantial performance gains and also showed strong generalization capability in out-of-domain tests .
Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging ( LiDAR ) point clouds acquired by mobile mapping systems ( MMS ) . This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction . For comparative evaluation , original intensity thresholding and deep learning using manually established labels strategies are also implemented .
The original quaternion convolution operation is presented and constructed to fully mix the information of color channels . The quaternion batch normalization and pooling operations are derived and designed in quaternion domain to further ensure the integrity of color information . Meanwhile , the knowledge of the attention mechanism is incorporated to boost the performance of the proposed QCNN .
Violations of economic rationality principles in choices between three or more options are critical for understanding the neural and cognitive mechanisms of decision-making . A recent study reported that the relative choice accuracy between two options decreases as the value of a third ( distractor ) option increases and attributed this effect to divisive normalization of neural value representations . In two preregistered experiments , a direct replication and an eye-tracking experiment , we assessed the replicability of this effect and tested an alternative account that assumes value-based attention to mediate the distractor effect .
To fully leverage the Near-Infrared channel , the traditional Red-Green-Blue channels , and Vegetation Index or its variants , we propose a Generalized Vegetation Index ( GVI ) , a lightweight module that can be easily plugged into many neural network architectures to serve as an additional information input . To smoothly train models with our GVI , we developed an Additive Group Normalization ( AGN ) module that does not require extra parameters of the prescribed neural networks . Our approach has improved the IoUs of vegetation-related classes by 0.9 — 1.3 percent and consistently improves the overall mIoU by 2 percent on our baseline .
We have considered various socioeconomic , psychological and academic factors to fully understand what a person 's life is during adolescence and how those factors impact their academic performance . Using pre-processing techniques such as feature selection , data balancing , discretization and normalization , and classification techniques such as Random Forest , Artificial Neural Net ( ANN ) , and Naïve Bayes we have developed different models to predict academic performance and also investigated prominent patterns in this study .
“ Batch normalization ” also centers activations with the goal to counter the internal covariate shift . Also the Projected Natural Gradient Descent algorithm ( PRONG ) centers the activations by implicitly whitening them .
Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first - order pooling . Recent works have shown matrix square root normalization plays a central role in achieving state - of - the - art performance . However , existing methods depend heavily on eigendecomposition ( EIG ) or singular value decomposition ( SVD ) , suffering from inefficient training due to limited support of EIG and SVD on GPU .
B - CNN is one of the first end - to - end covariance pooling ConvNets . It performs element - wise square root normalization followed by normalization for covariance matrix , achieving impressive performance in FGVC task . Improved B - CNN shows that additional matrix square root normalization before element - wise square root and normalization can further attain large improvement .
Implementation of iSQRT - COV Meta - layer We encapsulate our code in three computational blocks , which implement forward & backward computation of pre - normalization layer , Newton - Schulz iteration layer and post - compensation layer , respectively . The code is written in C ++ based on NVIDIA on top of CUDA toolkit 8.0 .
However , the model is so numerically sensitive that we can not operate a stable training on it the original training settings . A possible reason is that , since the feature space of CNN changes rapidly during the training procedure , linear transformation without any normalization may not be able to incorporate the information contained in the feature vector leaked from D. Here we present more examples for illustrating the interaction of WORKER and MANAGER to support our claims in the main text as below .
Corresponding expert segmentations were hidden and results had to be submitted to an online evaluation platform . Similar to BRATS , the only pre - processing that we applied is the normalization of each image to the zero - mean and unit variance . subsubsection : Experimental Setting
In this way , the computation of becomes slightly more efficient than the one shown in Figure [ reference ] , for we save the fusion operation . Finally , an additional saving of the normalization step derives from using the stored in the backward implementation of BN rather than recomputing it from . To distinguish the efficient backward implementation of BN from the standard one we write in place of ( cyan - colored , see additionally § [ reference ] ) .
We train on this objective by including multiple paragraphs from the same context in each mini - batch . This is similar to simply feeding the model multiple paragraphs from each context concatenated together , except that each paragraph is processed independently until the normalization step . The key idea is that this will force the model to produce scores that are comparable between paragraphs , even though it does not have access to information about the other paragraphs being considered .
Tab . [ reference ] shows how each normalization step helps for 3D detection . We see that both frustum rotation ( such that frustum points have more similar XYZ distributions ) and mask centroid subtraction ( such that object points have smaller and more canonical XYZ ) are critical .
However , they are harder to optimize compared to feed - forward networks due to challenges like variable length input sequences , repeated application of the same transition operator at each time step , and largely - dense embedding matrix that depends on the vocabulary size . Due to these optimization challenges in RNNs , the application of batch normalization and its variants ( layer normalization , recurrent batch normalization , recurrent normalization propagation ) have not been as successful as their counterparts in feed - forward networks , although they do considerably provide performance gains . Similarly , naive application of dropout has been shown to be ineffective in RNNs .
Similarly as a substitute for batch normalization , layer normalization normalizes the hidden units within each sample to have zero mean and unit standard deviation . Recurrent batch normalization applies batch normalization but with unshared mini - batch statistics for each time step . and on the other hand show that activity regularization ( AR ) and temporal activation regularization ( TAR ) are also effective methods for regularizing LSTMs .
It achieves test accuracy of 96.1 with only 44k parameters . Note that the previous state - of - the - art utilizes the recurrent batch normalization . The version without it has a much lower performance compared to all the dilated models .
Normalization techniques As described in Section 4 , we use the exponential function followed by pixel - wise normalization across channels in several stages of the CRF - RNN . Since this operation has a tendency to result in small gradients with respect to the input when the input value is large , we conducted several experiments where we replaced this by a rectifier linear unit ( ReLU ) operation followed by a normalization across the channels .
al showed that a the VGG - 16 very deep architecture , when trained with a broad and deep dataset containing one thousand examples of 2622 subjects , outperformed networks with specialized networks and ensembles on YouTubeFaces . Second , many top performing approaches use some form of pose normalization such as 2D / 3D alignment to warp the facial landmarks into a canonical frontal pose . Finally , many approaches use metric learning in the form of triplet loss similarity or joint Bayesian metric learning for the final loss to learn an optimal embedding for verification .
We report average prediction accuracies and standard deviations . For Patchy - san ( referred to as PSCN ) , we used - dimensional WL normalization , a width equal to the average number of nodes ( see Table [ reference ] ) , and receptive field sizes of and . For the experiments we only used node attributes .
In order not to use words in irrelevant passages , our model introduces the concept of combined attention SunHLLMT18 . While the original technique combines the word and sentence level attentions , our model combines the passage - level relevance and word - level attentions by using simple scalar multiplication and re - normalization . The updated word attention is :
Recently , alternative methods for normalizing feature activations in neural networks have been proposed . Among them , group normalization has been shown to yield similar , in some domains even superior performance to batch normalization . All these methods utilize a learned affine transformation after the normalization operation to increase representational power .
They observe that only a handful of clients ( enrolled users in the gallery ) actually contribute disproportionately to recognition errors . While prior literature attempting to reduce this effect focuses on either client-specific score normalization or client-specific decision strategies , in this study , we explore a novel category of approaches : group-specific score normalization . While client-specific score normalization can be negatively impacted by the paucity of genuine score samples , group-specific score normalization is less affected since the matching score samples of different clients belonging to the same group are aggregated .
The caller makes the call by dialing or orally giving the telephone number of the call destination or by inputting an abbreviated dial code relating to the telephone number . The equipment is provided with a speaker verification system adopting group normalization scoring . The group normalization scoring provides a dynamic threshold value to a verification process and further resistance force to fluctuation in training utterance and verification utterance is supplied to the process . The fluctuation is due to the fluctuation of the size of the voice of the speaker or communication channel characteristics .
In particular , dilated convolution layers with symmetrically fixed rates are stacked to learn spatial attention . A channel attention mechanism with the cross-level group normalization and squeeze-and-excitation operation is proposed . Aligned attention is constructed with organized deformable filters .
When designing the lightweight network , Concatenated Rectified Linear Units ( CReLU ) and depthwise separable convolution operation were employed to reduce the computation cost and model size . When training the lightweight network from scratch , we employ Group Normalization ( GN ) in each convolution layer , which makes smoother optimization landscape and has more stable gradients . A serial of ablation experiments is conducted on the recently published large-scale Dataset for Object detection in Aerial images ( DOTA ) , and the results show that the proposed object detection methods with lightweight network trained from scratch achieves competitive performance but has smaller model size and lower computation cost .
A novel detection algorithm based on fully convolutional networks with fusion of multilayer convolutional features is proposed in this paper . This method predict directly the classification and bounding boxes of the objects in the aerial images based on pixel information , and improve the network framework by use of Group Normalizer and deformable convolution to promote accuracy of object detection . The experimental results on DOTA dataset with oriented bounding boxes show that the detection algorithm presented in this paper possesses fast detection speed and higher accuracy .
According to the feature normalization method , only the diversity problem of data distribution among features can be solved , and the problem of the large distribution difference of feature internal data can not be solved . According to the grouping normalization method , the advantage of an overall feature normalization method is retained , meanwhile , the problem of the overlarge distribution scale in the feature data is solved , and the classification accuracy is improved . The feature grouping normalization method has strong robustness .
This paper proposes a novel normalization group strategy ( NGS ) to extend brain storm optimization ( BSO ) for power electronic circuit ( PEC ) design and optimization . As different variables in different dimensions of the PEC represent different circuit components such as resistor , capacitor , or inductor , they have different physical significances and various search space that are even not in comparable range .
Quantum Field Theory ( QFT ) [ 11 is the basis of the modern high energy physics [ 21 . The property of the asymptotic freedom in some QFT models allows us to use perturbation theory ( PT ) [ 1.1 , which in combination with the method of re-normalization group ( RG ) [ 1 ] is the main calculat ional tool of the QFT . Usually , the applied QFT deals with Feynman integrals ( FI ) .
Synchronized batch - normalization slows down training significantly and training on GPUs is also impractical for most people . Therefore , group normalization has been recently proposed so that instance level recognition tasks can benefit from another form of normalization in a low batch setting during training . With SNIPER , we show that the image resolution bottleneck can be alleviated for instance level recognition tasks .
The encoder part uses ResNet blocks , where each block consists of two convolutions with normalization and ReLU , followed by additive identity skip connection . For normalization , we use Group Normalization ( GN ) , which shows better than BatchNorm performance when batch size is small ( bath size of 1 in our case ) . We follow a common CNN approach to progressively downsize image dimensions by 2 and simultaneously increase feature size by 2 .
The invention discloses a data control method based on data platforms . The data control method includes : ( 1 ) acquiring data of a plurality of the data platforms and integrating the data , wherein the integrated data includes user data of the data platforms , original data of data items , multi-dimensional descriptions of user behavior , multi-dimensional descriptions of the data items , online data and offline data ; ( 2 ) processing the integrated data in a distributed processing frame mode and performing normalization operation , standardization operation and data cleaning operation , wherein the normalization operation refers to performing normalization operation to numerical data , the standardization operation refers to organizing the data in structuralization , keeping the data integrity , reducing redundancy and increasing the uniformity of the data , and the data cleaning operation refers to perform the data cleaning operation to incomplete data , wrong data and repeated data ; and ( 3 ) extracting the processed data and displaying the data . The data control method based on the data platforms improves the speed of data searching through a novel data control mode .
Biometric identification technology based on hand vein subcutaneous network structure appears as a promising technique for personal recognition . The contrast of hand vein image is enhanced by using Wiener filter , normalized mode , gray level stretching , sharpening and so on . After binary image was obtained , four-domain region growing algorithm was presented to differentiate and get rid of the noise areas .
The manner in which floating-point arithmetic is handled in this mode is discussed . Several numerical experiments using this mode are described and comparisons are made with the ordinary “ normalized mode . ” Examples include power series evaluation , linear equations solution , determinant evaluation and matrix inversion .
When only two spatially adjacent channels of the seven-channel system were active , overall performance was substantially better with a geometrically spaced spatial configuration ( with far-field talkers at −90 ° , −30 ° , −10 ° , 0 ° , + 10 ° , + 30 ° , and + 90 ° azimuth ) or a hybrid near-far configuration ( with far-field talkers at −90 ° , −30 ° , 0 ° , + 30 ° , and + 90 ° azimuth and near-field talkers at ±90 ° ) than with a more conventional linearly spaced configuration ( with far-field talkers at −90 ° , −60 ° , −30 ° , 0 ° , + 30 ° , + 60 ° , and + 90 ° azimuth ) . When all seven channels were active , performance was generally better with a “ better-ear ” normalization scheme that equalized the levels of the talkers in the more intense ear than with a default normalization scheme that equalized the levels of the talkers at the center of the head . The best overall performance in the seven-talker task occurred when the hybrid near-far spatial configuration was combined with the better-ear normalization scheme .
Using case studies from recent single-cell RNA-seq , Nanostring and Metagenomics studies , we highlight its general utility and , in particular , show a ) the ability to correctly model single-cell RNA-seq data and do comparisons with 1/5th the amount of sequencing currently used and b ) that the selection of suitable statistical tests strongly impacts the ability to detect biomarkers in Metagenomic studies . Furthermore , we demonstrate that a novel mode-based normalization employed in EDDA uniformly improves in robustness over existing approaches ( 10 - 20 % ) and increases precision to detect differential abundance by up to 140 % .
The invention relates to a steering arm shaft of an automobile power steering gear , in particular to a manufacturing method for a gear on the steering arm shaft of the automobile power steering gear . A forging die is included ; the method includes the following steps : metal blank is heated to 1,150DEG C to 1,200 DEG C and is put into a blank forming die of the forging die , and the volume of the blank is reasonably distributed by forging to obtain a forging bar of the steering arm shaft ; the forging bar of the steering arm shaft is taken out and reheated to 850 DEG C and 900 DEG C ; the heated forging bar of the steering arm shaft is taken out and put in the die impression of the forging die for molding and forging to obtain a molded steering arm shaft with gear teeth on the shaft body ; after forging , the gear teeth are reserved with cutting amount for machining ; a normalizing mode is adopted for the heat treatment of the molded steering arm shaft ; cutting is carried out to the cutting amount reserved on the gear teeth , and superficial treatment is carried out after cutting . The manufacturing method of the invention can reduce cutting amount , shorten cutting time , improve the strength of the gear and lower manufacturing cost .
subsection : Coupling structure To match the same order of magnitude , we apply a normalization operation to the output of local and global FCN before they are combined together . We explored two different methods to perform normalization : an L2 normalization layer or a 1x1 convolutional layer to model the scale .
In this paper , we explore the structure of neural loss functions , and the effect of loss landscapes on generalization , using a range of visualization methods . First , we introduce a simple " filter normalization " method that helps us visualize loss function curvature , and make meaningful side-by-side comparisons between loss functions . Then , using a variety of visualizations , we explore how network architecture affects the loss landscape , and how training parameters affect the shape of minimizers .
We develop color normalization filters based on a total of 319 images which normalize color of images using the HSV color system . We determined that the color characteristics of the peripheral part of the tumors significantly influence the color normalization and confirmed that the developed normalization filter achieved satisfactory normalization performance as evaluated by a cross-validation test .
This paper proposes a methodology of the pixel-based algorithm for forest change detection using PALSAR-2 ScanSAR data . Frost filter is applied to reduce speckle noise and normalization filter is implemented to reduce ground moisture effects . In case of Peru and Paraguay , the user ’s accuracy of developed algorithm was almost same as those of JJ-FAST version-2 while the producer ’s accuracy was 3 times better than JJ-FAST latest version ( version-2 ) .
In addition , sequential thinning and thickening morphological method is used to construct the skeleton model . The proposed hybrid approach is compared with the normalized filter . As a result , the proposed approach produces better skeleton model with less influential effect on shadow and illumination .
A study has been carried out using MODTRAN 4.0 radiative transfer code simulations to calculate the brightness temperatures expected at the Advanced Very High Resolution Radiometer ( AVHRR ) on board the National Oceanic and Atmospheric Administration NOAA satellites series ( 7 , 9 , 11 , 12 , 14 , 15 , 16 , 17 , 18 and 19 ) by using Gaussian Referential Filter ( GRF ) and Gaussian Filter ( GF ) instead of filter Normalized Filter obtained from NOAA agency . The outputs of applying MODTRAN 4.0 are values of atmospheric parameters obtained by mathematical convolution using GRF and GF Filters . A detailed analysis of the total error in LST , d Total(Ts ) , in function of AVHRR/NOAA satellites , shows that the algorithms are able to estimate accurate LST between a minimum of 1.256 K and a maximum of 1.415 K with amplitude of about 0.159 K. The validations show also that the algorithms are capable to produce LST with a standard deviation lower than 1.554 K and a Root Mean Square Error ( RMSE ) lower than 1.558 K. This result gives the opportunity to use the filter GF instead of filter Normalized Filter obtained from NOAA agency , in other studies by creation of GF filters centered in any region of the electromagnetic .
This filter is applied on the logarithmic energy outputs of Mel spaced filter banks . We also demonstrate the effectiveness of the proposed channel normalizing filter in improving speaker verification performance in mismatched conditions . GMM-based systems often use thousands of mixture components and hence require a large number of parameters to characterize each target speaker .
However , they either do not match the performance of BN for large batches , or still exhibit degradation in performance for smaller batches , or introduce artificial constraints on the model architecture . In this paper we propose the Filter Response Normalization ( FRN ) layer , a novel combination of a normalization and an activation function , that can be used as a replacement for other normalizations and activations . Our method operates on each activation channel of each batch element independently , eliminating the dependency on other batch elements .
We formulate both learning and inference as constrained optimization problems and solve them using augmented Lagrange method . The experiment results demonstrate that the normalization of filters at different scales helps to achieve fast convergence in learning stage and obtain superior performance in image restoration , e.g. , image denoising and image inpainting .
A normalized digital two-pair has unity L p - scaling norms at all pertinent internal nodes and the two output nodes . A simple technique is proposed to derive the normalized filter structure recently forwarded by Gray and Markel . Additional normalized digital two-pairs are also derived .
In this paper , we propose a simple yet effective parametric adaptive filter ( PAF ) and demonstrate its excellent performance in acoustic echo cancellation applications . Specifically , the proposed PAF decomposes an adaptive filter into three components : a normalized finite impulse response filter , a gain factor and a time-shift factor . With a novel update scheme for the PAF , these three components can be adjusted with different step-size respectively .
Most defending methods only focus on tuning the DNN itself , but we propose a novel defending method which modifies the input data to detect the adversarial examples . We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work . The framework gives the decision by comparing the classification results of original input and multiple normalized inputs .
Deformable convolution is capable of learning receptive fields adaptively , as shown in Figure [ reference ] , [ reference ] and Table [ reference ] . Atrous convolution [ ] It increases a normal filter ’s stride to be larger than and keeps the original weights at sparsified sampling locations .
Performance of different processing schemes in seismic noise cross-correlations Extended Batch Normalization Why Regularized Auto-Encoders learn Sparse Representation? SED, a normalization free method for DNA microarray data analysis Deep Normalization for Speaker Vectors Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds Quaternion Convolutional Neural Network for Color Image Classification and Forensics Value-based attention but not divisive normalization influence decisions with multiple alternatives Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture Prediction of HSC examination performance using socioeconomic, psychological and academic factors Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization Long Text Generation via Adversarial Training with Leaked Information Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation In-place Activated BatchNorm for Memory-Optimized Training of DNNs Simple and Effective Multi-Paragraph Reading Comprehension Frustum PointNets for 3D Object Detection from RGB-D Data Fraternal Dropout Dilated Recurrent Neural Networks Conditional Random Fields as Recurrent Neural Networks Template adaptation for face verification and identification Learning Convolutional Neural Networks for Graphs Multi-style Generative Reading Comprehension An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation Group-specific score normalization for biometric systems Speaker verification method using group normalization scoring HAR-Net: Joint Learning of Hybrid Attention for Single-Stage Object Detection Training lightweight network from scratch for efficient object detection in aerial images Multi-target Detection for Aerial Images Based on Fully Convolutional Networks Feature grouping normalization method for cognitive state recognition Normalization group brain storm optimization for power electronic circuit optimization Algebraic perturbative calculations in high energy physics: Methods, algorithms, computer programs and physical applications SNIPER: Efficient Multi-Scale Training 3D MRI brain tumor segmentation using autoencoder regularization Data control method based on data platforms Study on algorithm for skeleton features extraction of hand vein image Significance arithmetic on a digital computer Optimizing the spatial configuration of a seven-talker speech display The importance of study design for detecting differentially abundant features in high-throughput experiments Method for producing upper gear tooth of steering arm shaft of automotive power steering gear CoupleNet: Coupling Global Structure with Local Parts for Object Detection Visualizing the Loss Landscape of Neural Nets Automated color normalization for dermoscopy images Pixel-Based Deforestation Detection Algorithm for ALOS-2/PALSAR-2 Stereo Vision Human Motion Detection and Tracking in Uncontrolled Environment Split-Window LST Algorithms Estimation From AVHRR/NOAA Satellites (7, 9, 11, 12, 14, 15, 16, 17, 18, 19) Using Gaussian Filter Function Data-Driven Temporal Filters and Alternatives to GMM in Speaker Verification Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks Normalized filter pool for prior modeling of nature images Realization of digital transfer functions using cascaded normalized two-pairs An adaptive filter with gain and time-shift parameters for echo cancellation Detecting Adversarial Examples in Deep Neural Networks using Normalizing Filters Deformable Convolutional Networks
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ICML ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition AAAI Medical Image Anal. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ACL 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICLR NIPS 2015 IEEE International Conference on Computer Vision (ICCV) Image Vis. Comput. ICML ACL CVPR Workshops GECCO (Companion) ISSAC NeurIPS BrainLes@MICCAI 2017 IEEE International Conference on Computer Vision (ICCV) ICIP IGARSS ICASSP ISCSLP 2017 IEEE International Conference on Computer Vision (ICCV)
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