During training , dropout randomly discards a portion of the neurons to avoid overfitting . This paper presents an enhanced dropout technique , which we call multi-sample dropout , for both accelerating training and improving generalization over the original dropout . The original dropout creates a randomly selected subset ( called a dropout sample ) from the input in each training iteration while the multi-sample dropout creates multiple dropout samples . Results from the simulation show that , the stochastic behavior can not be avoided but we have to find way to deal with it . The proposed dropout method improves the state-of-the-art of applied deep neural networks on the benchmark dataset . The ultralow-dropout LED flashlight is characterized by comprising a 2.7 - 6V direct-current power supply , an ultralow-dropout current stabilizing circuit , light-emitting components LED2-LED4 and a battery working state indication circuit , wherein the selected model of an ultralow-dropout current stabilizer IC1 in the ultralow-dropout current stabilizing circuit is AMC7135 . In order to overcome the defects of large size , high power consumption and low luminance of a common flashlight with a battery and an incandescent light bulb , the ultralow-dropout LED flashlight adopts the special ultralow-dropout LED drive integrated circuit ( AMC7135 ) , three white light-emitting diodes with high luminance can be lighted up with one lithium battery , and the ultralow-dropout LED flashlight has the characteristics of electric power saving , convenience in use , long service life and the like . The technical scheme can be also applied to a miner 's lamp and emergency illuminance or applied to solar electric power supply illumination and the like . This paper introduces a deep learning architecture for a marine virtual sensor application that is able to utilize input quality information . We propose a novel input quality based dropout layer to take advantage of Information about the quality of the input sensors . The virtual sensor models are built upon convolutional and recurrent long short-term memory layers . In this work , based on the recently released NTCD-TIMIT audio-visual corpus , we address the challenges of AVSR through three aspects : 1 ) optimal integration of acoustic and visual information ; 2 ) robust performance with multi-condition training ; 3 ) robust modeling against missing visual information during decoding . We propose a bimodal-DFSMN to jointly learn feature fusion and acoustic modeling , and utilize a per-frame dropout approach to enhance the robustness of AVSR system against the missing of visual modality . In the experiments , we construct two setups based on the NTCD-TIMIT corpus that consists of 5 hours clean training data and 150 hours multi-condition training data , respectively . Previous strategies , such as using shape priors , are not effective with such large dropout regions . This paper proposes a dropout modeling strategy , which can bridge large dropout regions and segment the endocardium when used along with shape priors . The segmentation is formulated as an active contour in a Maximum-APosteriori ( M.A.P. ) framework with explicit priors for the dropout function and shape . To tackle this issue , we propose self-augmentation that consolidates self-mix and self-distillation . Specifically , we exploit a regional dropout technique called self-mix , in which a patch of an image is substituted into other values in the same image . Then , we employ a backbone network that has auxiliary branches with its own classifier to enforce knowledge sharing . A big challenge of training such a deep network is the limited training data , and exploring RGB-only stream significantly exaggerates the difficulty . We therefore propose a set of algorithmic techniques to train this model effectively , e.g. , data augmentation , dynamic frame dropout and gradient injection . The experiments demonstrate that our RGB-only solution surpasses the state-of-the-art approaches that all exploit RGB-D video streams by a notable margin . A capacitor-less low-power low-dropout regulator ( LDR ) with a slew-rate-enhanced circuit ( SRE ) was proposed . The SRE uses six transistors to constitute two comparators and two auxiliary transistors . This paper presents an ultra-low voltage and ultra-low power PVT tolerant digital PLL with a semi-digital low dropout regulator ( LDO ) . A low cost integrated temperature compensation circuit ( TCC ) is proposed and implemented by combining with a proposed ΑΣ LDO to reduce temperature variation of the digitally-controlled relaxation oscillator ( DCRXO ) . We also show that post-hoc calibration methods like Isotonic Calibration sometimes compound miscalibration whereas our method provides consistently better calibrations . We provide empirical results demonstrating that the proposed quantile regularizer significantly improves calibration for regression models trained using approaches , such as Dropout VI and Deep Ensembles . subsection : Regularization For regularization , we employ two commonly used techniques : dropout and L2 weight regularization . We apply dropout to prevent co - adaptation . In this case , the feature of text is a 1 , 720 dimensional vector . Dropout [ reference ] ) with the keep rate 0.75 and L2 regularization are performed to avoid overfitting . For the generator , we adopt LSTM [ reference ] as the architectures of MANAGER and WORKER to capture the sequence context information . section : System improvements In this section we describe three different acoustic models that were trained on 2000 hours of English conversational telephone speech : recurrent nets with maxout activations and annealed dropout , very deep convolutional nets with 3 3 kernels , and bidirectional long short - term memory nets operating on FMLLR and i - vector features . All models are used in a hybrid HMM decoding scenario by subtracting the logarithm of the HMM state priors from the log of the softmax output scores . We compare our Advesarial Spatial Dropout Network with various dropout / occlusion strategy in training using the AlexNet architecture . The first simple baseline we try is random spatial dropout on the feature after RoI - Pooling . For a fair comparison , we mask the activations of the same number of neurons as we do in the ASDN network . We apply this stochastically by assigning a distribution pR ( · ) on the frequency truncation radius ( for simplicity we apply the same truncation on both axes ) , sampling from this a random radius at each iteration , and wiping out all frequencies outside the square of that size . Note that this can be regarded as an application of nested dropout ( Rippel et al . , 2014 ) on both dimensions of the frequency decomposition of our input . In practice , we have had success choosing pR ( · ) Research towards Bayesian networks aims to alleviate this limitation . An example is the recent work of who show that model confidence can be estimated via sampling the dropout mask . A general point should be made about the performance drop observed when our system is applied on test datasets of BRATS and ISLES in comparison to its cross - validated performance on the training data . This deep LSTM uses Highway connections Srivastava2015TrainingVD between layers and variational recurrent dropout Gal2016ATG . This deep representation is then projected using a final dense layer followed by a softmax activation to form a distribution over all possible tags . The Gaussian distribution is used with a mean of 0 and variance of 1 to generate the random values . single dropout mask and we use dropout on the input and the states of the LSTM . We defer further details regarding training the models to section A of the appendix . We refer to our baseline network as variational dropout LSTM , or VD - LSTM in short . section : EMPIRICAL VALIDATION FOR THE THEORY OF REUSING WORD EMBEDDINGS It had a 784 - 500 - 500 - 2000 architecture and was trained using greedy layer - wise Contrastive Divergence learning . Instead of fine - tuning it with the usual backpropagation algorithm , we used the dropout version of it . Dropout rate was same as before : 50 % for hidden units and 20 % for visible units . We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers A block version of the Shake method for heavy atom simulation in biological systems is presented in this paper . The method solves successively , independent blocks of constraints of small size by a Newton method . Also , RICAP mixes the class labels of the four images and enjoys a benefit similar to label smoothing . We evaluated RICAP with current state-of-the-art CNNs ( e.g. , shake-shake regularization model ) and achieved a new state-of-the-art test error of 2.23 % on CIFAR-10 among competitive data augmentation techniques such as cutout and mixup . We also confirmed that deep CNNs with RICAP achieved better results on CIFAR-100 and ImageNet than those results obtained by other techniques . Abstract Several aspects of the convergence speed for the SHAKE iterative technique are reviewed . Two examples of molecular geometry which are known to have slow SHAKE convergence are used to show that simple changes to the procedure can result in a five-fold improvement in convergence speed . The regularization parameter is selected by the L-curve method . Numerical experiments show that the lp sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets , although a target with a small absorption coefficient may disappear due to the strong effect of the lp sparsity regularization when the value of p is too small . The lp sparsity regularization with small p values strongly localizes the target , and the reconstructed region of the target becomes smaller as the value of p decreases . A phantom experiment validates the numerical simulations . Our objective is to asses the performance of covariance matrix regularization methods on real world data , to provide points of reference for future applications . We analyse the following estimators : OAS , Rao-Blackwell-Ledoit-Wolf , Ledoit-Wolf in two versions , and Thresholding on data from several publicly available datasets ( K9 , Isolet , Slice , Gistette , S1 ADL1 ) . In this paper , we are proposing a technique called Bi-directional Random Forest Granger causality . This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information . We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets , i.e. , fMRI and HeLa cell . Here , we propose an algebraic iterative reco nstruction-re-projection ( AIRR ) algorithm with the shear l t regularization . The AIRR coupled with the shearlet regularization in image s pace attains a better estimation in the projection space and yiel ed a higher performance based on subjective and objective quali ty metrics . We approach this from two directions : First , we provide a general model of augmentation as a Markov process , and show that kernels appear naturally with respect to this model , even when we do not employ kernel classification . Next , we analyze more directly the effect of augmentation on kernel classifiers , showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components . These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model , and the resulting analyses provide novel connections between prior work in invariant kernels , tangent propagation , and robust optimization . This paper is concerned with low-energy Earth- Moon transfer trajectory via Lyapunov orbit located on Earth- Moon libration point LI . The circular restricted three-body problem is used as the model for Earth-Moon system , and Birkhoff regularization is used to avoid singularities when the distance between spacecraft and primary is very short . Using both analytical and numerical methods , Lyapunov orbits near the collinear libration point LI in CR3BP are computed . On each scale , regularization Gauss – Newton method is carried out , which is stable and fast , until the optimum solution of original inverse problem is found . The results of numerical simulations demonstrate that the method is a widely convergent optimization method and exhibits the advantages of conventional regularization Gauss – Newton method methods on computational efficiency and precision . We present recent developments in data processing for velocity field estimation and visualization originating from computer vision . We review the current paradigm of PIV data processing , based on window correlation , and the regularization or variational approach which is dominant in optical flow estimation . We propose a novel unifying framework via the optimization of a compound regularized criterion written in terms of a dense displacement ( or velocity ) field . We introduce two classes of stochastic approximation methods , each of which requires exactly one projection step at every iteration , and provide convergence analysis for each of them . Of these , the first is a stochastic iterative Tikhonov regularization method which necessitates the update of the regularization parameter after every iteration . The second method is a stochastic iterative proximal-point method , where the centering term is updated after every iteration . Unfortunately , the accuracy with which neural sources can be localized is limited by the highly illposed nature of the inverse problem . A large number of inverse methods have been proposed that deal with this illposedness using a range of different modeling and regularization procedures . Here we describe an objective task-based framework for comparing different inverse methods . For joint training of transform network and mask network , we incorporate the adversarial attribute loss , identity aware adaptive perceptual loss and VGG - FACE based identity loss . Furthermore , a denoising network is presented to serve for perceptual regularization to suppress the artifacts in transfer result , while an attribute ratio regularization is introduced to constrain the size of attribute relevant region . Our DIAT can provide a unified solution for several representative facial attribute transfer tasks , e . g . , expression transfer , accessory removal , age progression and gender transfer , and can be extended for other face enhancement tasks such as face hallucination . ( [ reference ] ) is defined on the denoising network , and thus is named as perceptual regularization . Attribute ratio regularization . The size of attribute relevant region varies for different attributes . The relation with expectation - linear dropout has been discussed in Section [ reference ] . Here we perform experiments to study the difference in performance when using the ELD regularization versus our regularization ( FD ) . In addition to ELD , we also study a modification ( ELDM ) of ELD which applies target loss to both copies of LSTMs in ELD similar to FD ( notice in their case they only have dropout on one LSTM ) . subsection : Randomization Baseline The proposed modification can also be interpreted as a form of Shake - Shake regularization proposed in . In this regularization strategy , random weights are sampled during forward and backward passes for weighing the various branches in a multi - branch network . subsection : Objective : Towards Regularized GANs In this paper , we attempt to develop regularization theory and algorithms for a novel Loss - Sensitive GAN ( LS - GAN ) . Specifically , we introduce a loss function to quantify the quality of generated samples . The authors show that convolution is a way of introducing sparsity and parameter tying and that HypER can be understood in terms of tensor factorization up to a non - linearity , thus placing HypER closer to the well established family of factorization models . The drawback of HypER is that it sets most elements of the core weight tensor to 0 , which amounts to hard regularization , rather than letting the model learn which parameters to use via a soft regularization approach . Scoring functions of all models described above and TuckER are summarized in Table [ reference ] . These methods either entail heavy computational overheads on training or require auxiliary networks that are often task - specific that can not be easily extended to other frameworks . In this work , we propose a mode seeking regularization method that can be applied to cGANs for various tasks to alleviate the mode collapse problem . Given two latent vectors and the corresponding output images , we propose to maximize the ratio of the distance between images with respect to the distance between latent vectors . Influenced by , our goal here is to make sure that fraternal dropout outperforms existing methods not simply because of extensive hyper - parameter grid search but rather due to its regularization effects . Hence , in our experiments we leave a vast majority of hyper - parameters used in the baseline model unchanged i.e. embedding and hidden states sizes , gradient clipping value , weight decay and the values used for all dropout layers ( dropout on the word vectors , the output between LSTM layers , the output of the final LSTM , and embedding dropout ) . However , a few changes are necessary : the coefficients for AR and TAR needed to be altered because fraternal dropout also affects RNNs activation ( as explained in Subsection [ reference ] )
During training , dropout randomly discards a portion of the neurons to avoid overfitting . This paper presents an enhanced dropout technique , which we call multi-sample dropout , for both accelerating training and improving generalization over the original dropout . The original dropout creates a randomly selected subset ( called a dropout sample ) from the input in each training iteration while the multi-sample dropout creates multiple dropout samples .
Results from the simulation show that , the stochastic behavior can not be avoided but we have to find way to deal with it . The proposed dropout method improves the state-of-the-art of applied deep neural networks on the benchmark dataset .
The ultralow-dropout LED flashlight is characterized by comprising a 2.7 - 6V direct-current power supply , an ultralow-dropout current stabilizing circuit , light-emitting components LED2-LED4 and a battery working state indication circuit , wherein the selected model of an ultralow-dropout current stabilizer IC1 in the ultralow-dropout current stabilizing circuit is AMC7135 . In order to overcome the defects of large size , high power consumption and low luminance of a common flashlight with a battery and an incandescent light bulb , the ultralow-dropout LED flashlight adopts the special ultralow-dropout LED drive integrated circuit ( AMC7135 ) , three white light-emitting diodes with high luminance can be lighted up with one lithium battery , and the ultralow-dropout LED flashlight has the characteristics of electric power saving , convenience in use , long service life and the like . The technical scheme can be also applied to a miner 's lamp and emergency illuminance or applied to solar electric power supply illumination and the like .
This paper introduces a deep learning architecture for a marine virtual sensor application that is able to utilize input quality information . We propose a novel input quality based dropout layer to take advantage of Information about the quality of the input sensors . The virtual sensor models are built upon convolutional and recurrent long short-term memory layers .
In this work , based on the recently released NTCD-TIMIT audio-visual corpus , we address the challenges of AVSR through three aspects : 1 ) optimal integration of acoustic and visual information ; 2 ) robust performance with multi-condition training ; 3 ) robust modeling against missing visual information during decoding . We propose a bimodal-DFSMN to jointly learn feature fusion and acoustic modeling , and utilize a per-frame dropout approach to enhance the robustness of AVSR system against the missing of visual modality . In the experiments , we construct two setups based on the NTCD-TIMIT corpus that consists of 5 hours clean training data and 150 hours multi-condition training data , respectively .
Previous strategies , such as using shape priors , are not effective with such large dropout regions . This paper proposes a dropout modeling strategy , which can bridge large dropout regions and segment the endocardium when used along with shape priors . The segmentation is formulated as an active contour in a Maximum-APosteriori ( M.A.P. ) framework with explicit priors for the dropout function and shape .
To tackle this issue , we propose self-augmentation that consolidates self-mix and self-distillation . Specifically , we exploit a regional dropout technique called self-mix , in which a patch of an image is substituted into other values in the same image . Then , we employ a backbone network that has auxiliary branches with its own classifier to enforce knowledge sharing .
A big challenge of training such a deep network is the limited training data , and exploring RGB-only stream significantly exaggerates the difficulty . We therefore propose a set of algorithmic techniques to train this model effectively , e.g. , data augmentation , dynamic frame dropout and gradient injection . The experiments demonstrate that our RGB-only solution surpasses the state-of-the-art approaches that all exploit RGB-D video streams by a notable margin .
A capacitor-less low-power low-dropout regulator ( LDR ) with a slew-rate-enhanced circuit ( SRE ) was proposed . The SRE uses six transistors to constitute two comparators and two auxiliary transistors .
This paper presents an ultra-low voltage and ultra-low power PVT tolerant digital PLL with a semi-digital low dropout regulator ( LDO ) . A low cost integrated temperature compensation circuit ( TCC ) is proposed and implemented by combining with a proposed ΑΣ LDO to reduce temperature variation of the digitally-controlled relaxation oscillator ( DCRXO ) .
We also show that post-hoc calibration methods like Isotonic Calibration sometimes compound miscalibration whereas our method provides consistently better calibrations . We provide empirical results demonstrating that the proposed quantile regularizer significantly improves calibration for regression models trained using approaches , such as Dropout VI and Deep Ensembles .
subsection : Regularization For regularization , we employ two commonly used techniques : dropout and L2 weight regularization . We apply dropout to prevent co - adaptation .
In this case , the feature of text is a 1 , 720 dimensional vector . Dropout [ reference ] ) with the keep rate 0.75 and L2 regularization are performed to avoid overfitting . For the generator , we adopt LSTM [ reference ] as the architectures of MANAGER and WORKER to capture the sequence context information .
section : System improvements In this section we describe three different acoustic models that were trained on 2000 hours of English conversational telephone speech : recurrent nets with maxout activations and annealed dropout , very deep convolutional nets with 3 3 kernels , and bidirectional long short - term memory nets operating on FMLLR and i - vector features . All models are used in a hybrid HMM decoding scenario by subtracting the logarithm of the HMM state priors from the log of the softmax output scores .
We compare our Advesarial Spatial Dropout Network with various dropout / occlusion strategy in training using the AlexNet architecture . The first simple baseline we try is random spatial dropout on the feature after RoI - Pooling . For a fair comparison , we mask the activations of the same number of neurons as we do in the ASDN network .
We apply this stochastically by assigning a distribution pR ( · ) on the frequency truncation radius ( for simplicity we apply the same truncation on both axes ) , sampling from this a random radius at each iteration , and wiping out all frequencies outside the square of that size . Note that this can be regarded as an application of nested dropout ( Rippel et al . , 2014 ) on both dimensions of the frequency decomposition of our input . In practice , we have had success choosing pR ( · )
Research towards Bayesian networks aims to alleviate this limitation . An example is the recent work of who show that model confidence can be estimated via sampling the dropout mask . A general point should be made about the performance drop observed when our system is applied on test datasets of BRATS and ISLES in comparison to its cross - validated performance on the training data .
This deep LSTM uses Highway connections Srivastava2015TrainingVD between layers and variational recurrent dropout Gal2016ATG . This deep representation is then projected using a final dense layer followed by a softmax activation to form a distribution over all possible tags .
The Gaussian distribution is used with a mean of 0 and variance of 1 to generate the random values . single dropout mask and we use dropout on the input and the states of the LSTM .
We defer further details regarding training the models to section A of the appendix . We refer to our baseline network as variational dropout LSTM , or VD - LSTM in short . section : EMPIRICAL VALIDATION FOR THE THEORY OF REUSING WORD EMBEDDINGS
It had a 784 - 500 - 500 - 2000 architecture and was trained using greedy layer - wise Contrastive Divergence learning . Instead of fine - tuning it with the usual backpropagation algorithm , we used the dropout version of it . Dropout rate was same as before : 50 % for hidden units and 20 % for visible units .
We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers
A block version of the Shake method for heavy atom simulation in biological systems is presented in this paper . The method solves successively , independent blocks of constraints of small size by a Newton method .
Also , RICAP mixes the class labels of the four images and enjoys a benefit similar to label smoothing . We evaluated RICAP with current state-of-the-art CNNs ( e.g. , shake-shake regularization model ) and achieved a new state-of-the-art test error of 2.23 % on CIFAR-10 among competitive data augmentation techniques such as cutout and mixup . We also confirmed that deep CNNs with RICAP achieved better results on CIFAR-100 and ImageNet than those results obtained by other techniques .
Abstract Several aspects of the convergence speed for the SHAKE iterative technique are reviewed . Two examples of molecular geometry which are known to have slow SHAKE convergence are used to show that simple changes to the procedure can result in a five-fold improvement in convergence speed .
The regularization parameter is selected by the L-curve method . Numerical experiments show that the lp sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets , although a target with a small absorption coefficient may disappear due to the strong effect of the lp sparsity regularization when the value of p is too small . The lp sparsity regularization with small p values strongly localizes the target , and the reconstructed region of the target becomes smaller as the value of p decreases . A phantom experiment validates the numerical simulations .
Our objective is to asses the performance of covariance matrix regularization methods on real world data , to provide points of reference for future applications . We analyse the following estimators : OAS , Rao-Blackwell-Ledoit-Wolf , Ledoit-Wolf in two versions , and Thresholding on data from several publicly available datasets ( K9 , Isolet , Slice , Gistette , S1 ADL1 ) .
In this paper , we are proposing a technique called Bi-directional Random Forest Granger causality . This technique uses the random forest regularization together with the idea of reusing the time series data by reversing the time stamp to extract more causal information . We have demonstrated the effectiveness of our proposed method by applying it to simulated data and then applied it to two real biological datasets , i.e. , fMRI and HeLa cell .
Here , we propose an algebraic iterative reco nstruction-re-projection ( AIRR ) algorithm with the shear l t regularization . The AIRR coupled with the shearlet regularization in image s pace attains a better estimation in the projection space and yiel ed a higher performance based on subjective and objective quali ty metrics .
We approach this from two directions : First , we provide a general model of augmentation as a Markov process , and show that kernels appear naturally with respect to this model , even when we do not employ kernel classification . Next , we analyze more directly the effect of augmentation on kernel classifiers , showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components . These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model , and the resulting analyses provide novel connections between prior work in invariant kernels , tangent propagation , and robust optimization .
This paper is concerned with low-energy Earth- Moon transfer trajectory via Lyapunov orbit located on Earth- Moon libration point LI . The circular restricted three-body problem is used as the model for Earth-Moon system , and Birkhoff regularization is used to avoid singularities when the distance between spacecraft and primary is very short . Using both analytical and numerical methods , Lyapunov orbits near the collinear libration point LI in CR3BP are computed .
On each scale , regularization Gauss – Newton method is carried out , which is stable and fast , until the optimum solution of original inverse problem is found . The results of numerical simulations demonstrate that the method is a widely convergent optimization method and exhibits the advantages of conventional regularization Gauss – Newton method methods on computational efficiency and precision .
We present recent developments in data processing for velocity field estimation and visualization originating from computer vision . We review the current paradigm of PIV data processing , based on window correlation , and the regularization or variational approach which is dominant in optical flow estimation . We propose a novel unifying framework via the optimization of a compound regularized criterion written in terms of a dense displacement ( or velocity ) field .
We introduce two classes of stochastic approximation methods , each of which requires exactly one projection step at every iteration , and provide convergence analysis for each of them . Of these , the first is a stochastic iterative Tikhonov regularization method which necessitates the update of the regularization parameter after every iteration . The second method is a stochastic iterative proximal-point method , where the centering term is updated after every iteration .
Unfortunately , the accuracy with which neural sources can be localized is limited by the highly illposed nature of the inverse problem . A large number of inverse methods have been proposed that deal with this illposedness using a range of different modeling and regularization procedures . Here we describe an objective task-based framework for comparing different inverse methods .
For joint training of transform network and mask network , we incorporate the adversarial attribute loss , identity aware adaptive perceptual loss and VGG - FACE based identity loss . Furthermore , a denoising network is presented to serve for perceptual regularization to suppress the artifacts in transfer result , while an attribute ratio regularization is introduced to constrain the size of attribute relevant region . Our DIAT can provide a unified solution for several representative facial attribute transfer tasks , e . g . , expression transfer , accessory removal , age progression and gender transfer , and can be extended for other face enhancement tasks such as face hallucination .
( [ reference ] ) is defined on the denoising network , and thus is named as perceptual regularization . Attribute ratio regularization . The size of attribute relevant region varies for different attributes .
The relation with expectation - linear dropout has been discussed in Section [ reference ] . Here we perform experiments to study the difference in performance when using the ELD regularization versus our regularization ( FD ) . In addition to ELD , we also study a modification ( ELDM ) of ELD which applies target loss to both copies of LSTMs in ELD similar to FD ( notice in their case they only have dropout on one LSTM ) .
subsection : Randomization Baseline The proposed modification can also be interpreted as a form of Shake - Shake regularization proposed in . In this regularization strategy , random weights are sampled during forward and backward passes for weighing the various branches in a multi - branch network .
subsection : Objective : Towards Regularized GANs In this paper , we attempt to develop regularization theory and algorithms for a novel Loss - Sensitive GAN ( LS - GAN ) . Specifically , we introduce a loss function to quantify the quality of generated samples .
The authors show that convolution is a way of introducing sparsity and parameter tying and that HypER can be understood in terms of tensor factorization up to a non - linearity , thus placing HypER closer to the well established family of factorization models . The drawback of HypER is that it sets most elements of the core weight tensor to 0 , which amounts to hard regularization , rather than letting the model learn which parameters to use via a soft regularization approach . Scoring functions of all models described above and TuckER are summarized in Table [ reference ] .
These methods either entail heavy computational overheads on training or require auxiliary networks that are often task - specific that can not be easily extended to other frameworks . In this work , we propose a mode seeking regularization method that can be applied to cGANs for various tasks to alleviate the mode collapse problem . Given two latent vectors and the corresponding output images , we propose to maximize the ratio of the distance between images with respect to the distance between latent vectors .
Influenced by , our goal here is to make sure that fraternal dropout outperforms existing methods not simply because of extensive hyper - parameter grid search but rather due to its regularization effects . Hence , in our experiments we leave a vast majority of hyper - parameters used in the baseline model unchanged i.e. embedding and hidden states sizes , gradient clipping value , weight decay and the values used for all dropout layers ( dropout on the word vectors , the output between LSTM layers , the output of the final LSTM , and embedding dropout ) . However , a few changes are necessary : the coefficients for AR and TAR needed to be altered because fraternal dropout also affects RNNs activation ( as explained in Subsection [ reference ] )
Multi-Sample Dropout for Accelerated Training and Better Generalization Towards Stochasticity of Regularization in Deep Neural Networks Ultralow-dropout LED flashlight Input quality aware convolutional LSTM networks for virtual marine sensors Robust Audio-visual Speech Recognition Using Bimodal Dfsmn with Multi-condition Training and Dropout Regularization Segmentation of Rat Cardiac Ultrasound Images with Large Dropout Regions Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data Capacitor-less low-dropout regulator with slew-rate-enhanced circuit A 0.6V 50-to-145MHz PVT tolerant digital PLL with DCO-dedicated ΔΣ LDO and temperature compensation circuits in 65nm CMOS Quantile Regularization: Towards Implicit Calibration of Regression Models A C-LSTM Neural Network for Text Classification Long Text Generation via Adversarial Training with Leaked Information The IBM 2016 English Conversational Telephone Speech Recognition System A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection Spectral Representations for Convolutional Neural Networks Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Deep contextualized word representations Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Improving neural networks by preventing co-adaptation of feature detectors Quasi-Recurrent Neural Networks Parallel constrained molecular dynamics RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs On the convergence of the SHAKE algorithm Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization Experimental Evaluation of Selected Approaches to Covariance Matrix Regularization Inference of biological networks using Bi-directional Random Forest Granger causality Algebraic iterative reconstruction-reprojection (AIRR) method for high performance sparse-view CT reconstruction A Kernel Theory of Modern Data Augmentation Translunar low-energy trajectories via Earth-Moon L1 Lyapunov orbit The wavelet multiscale method for inversion of porosity in the fluid-saturated porous media Advanced Processing Methods for Image-Based Displacement Field Measurement Regularized Iterative Stochastic Approximation Methods for Stochastic Variational Inequality Problems Task-based comparison of inverse methods in magnetoencephalography Deep Identity-aware Transfer of Facial Attributes Fraternal Dropout Weighted Transformer Network for Machine Translation Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities TuckER: Tensor Factorization for Knowledge Graph Completion Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis Fraternal Dropout
4f9554a93dbbac467fb7da3ca61270d0c427f868 8900cc58ca69901005db2a6d73b373b8bbe2c141 707ebcc362aa4c888ae02eebf3bed073b63b6dea c82d07bcf006c83aff846922e9712344bd9037eb 6b488224f6abb21089aae826503a020fb59f02e5 2e6514169861f37d6287f4c40089e52428b87ebf dd173a09a856b204d3d596cb1ff5462dacee7aeb 74208450afbc88312d46e51d9df8f50a7b952237 fbef73554d7a7e2d04d7a223b3c9cf6cc1db4077 ebf6ca4387611f68ce50593926a4ed31a3629a19 ecbe4eaf35f6b3086cec03a25b94a11c205782cb 4da3f4dfd6dccb73c4988cb7f0b92b310bfa4cdf 043d1f7b26d0aa723bf2d63eb277d2b8be19bec0 c95fbe965f2aa391286488c82579fca20c1dc06b f6cf64b3523a0a96c889b5c6235cba4ff34afc0f 904eb415a4ad1d447c2bdef0437acabba206c745 d849949428a3e7f007e239b939ffcb030e094215 d1bae83a418e80db63cbf3bfbd5b8229daa1e3fd 494e63c4a333bf3be8a28dc0213b0e0d39c2ef04 d4555e0d04666c4a1fa443696870129f0d6259e0 030ee1efab83c77e028371cebf0644523845404d ba4a8215142d328300c1a4762309b96a8a27b4ef 510255b2e83440fbce008b199dc0dd635f5776fb 6da2ca4ad7059435ca9f0b4cc8906e9eaf119a0e
["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Mathematics"] ["Computer Science","Mathematics","Medicine"] ["Physics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Medicine","Computer Science"]
2019 2018 2015 2018 2019 2006 2020 2018 2011 2017 2020 2015 2018 2016 2017 2015 2017 2018 2018 2017 2012 2017 2000 2018 1991 2011 2015 2016 2016 2018 2007 2006 2009 2013 2006 2016 2018 2017 2019 2019 2019 2018
ICASSP CVPR Workshops ICMLA ISCAS ArXiv AAAI INTERSPEECH 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS Medical Image Anal. NAACL-HLT ACL ICLR ArXiv ICLR ACML ICAISC ROBIO ArXiv ICLR ArXiv International Journal of Computer Vision EMNLP/IJCNLP 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICLR
https://api.semanticscholar.org/4f9554a93dbbac467fb7da3ca61270d0c427f868 https://api.semanticscholar.org/8900cc58ca69901005db2a6d73b373b8bbe2c141 https://api.semanticscholar.org/707ebcc362aa4c888ae02eebf3bed073b63b6dea https://api.semanticscholar.org/c82d07bcf006c83aff846922e9712344bd9037eb https://api.semanticscholar.org/6b488224f6abb21089aae826503a020fb59f02e5 https://api.semanticscholar.org/2e6514169861f37d6287f4c40089e52428b87ebf https://api.semanticscholar.org/dd173a09a856b204d3d596cb1ff5462dacee7aeb https://api.semanticscholar.org/74208450afbc88312d46e51d9df8f50a7b952237 https://api.semanticscholar.org/fbef73554d7a7e2d04d7a223b3c9cf6cc1db4077 https://api.semanticscholar.org/ebf6ca4387611f68ce50593926a4ed31a3629a19 https://api.semanticscholar.org/ecbe4eaf35f6b3086cec03a25b94a11c205782cb https://www.semanticscholar.org/paper/10f62af29c3fc5e2572baddca559ffbfd6be8787 https://www.semanticscholar.org/paper/485552d2711868b54d5fcddc92c746b09afeab07 https://www.semanticscholar.org/paper/05d2700846c0323f79c1344aca5333994c7c03a5 https://www.semanticscholar.org/paper/bbb6ef63bec6db62e1bdfb5137ba7f7a42a507d4 https://www.semanticscholar.org/paper/0e37c8f19eefeb0c20d92f5cb4df4153077c116b https://www.semanticscholar.org/paper/7c2bcf6f32b05a04cd3444c030db743e5666af88 https://www.semanticscholar.org/paper/3febb2bed8865945e7fddc99efd791887bb7e14f https://www.semanticscholar.org/paper/cc5582606aef717855bb37c7aa35639da755cb13 https://www.semanticscholar.org/paper/424aef7340ee618132cc3314669400e23ad910ba https://www.semanticscholar.org/paper/1366de5bb112746a555e9c0cd00de3ad8628aea8 https://www.semanticscholar.org/paper/2d876ed1dd2c58058d7197b734a8e4d349b8f231 https://api.semanticscholar.org/4da3f4dfd6dccb73c4988cb7f0b92b310bfa4cdf https://api.semanticscholar.org/043d1f7b26d0aa723bf2d63eb277d2b8be19bec0 https://api.semanticscholar.org/c95fbe965f2aa391286488c82579fca20c1dc06b https://api.semanticscholar.org/f6cf64b3523a0a96c889b5c6235cba4ff34afc0f https://api.semanticscholar.org/904eb415a4ad1d447c2bdef0437acabba206c745 https://api.semanticscholar.org/d849949428a3e7f007e239b939ffcb030e094215 https://api.semanticscholar.org/d1bae83a418e80db63cbf3bfbd5b8229daa1e3fd https://api.semanticscholar.org/494e63c4a333bf3be8a28dc0213b0e0d39c2ef04 https://api.semanticscholar.org/d4555e0d04666c4a1fa443696870129f0d6259e0 https://api.semanticscholar.org/030ee1efab83c77e028371cebf0644523845404d https://api.semanticscholar.org/ba4a8215142d328300c1a4762309b96a8a27b4ef https://api.semanticscholar.org/510255b2e83440fbce008b199dc0dd635f5776fb https://api.semanticscholar.org/6da2ca4ad7059435ca9f0b4cc8906e9eaf119a0e https://www.semanticscholar.org/paper/2b507f659b341ed0f23106446de8e4322f4a3f7e https://www.semanticscholar.org/paper/415f18130edbe06e3e4806dfb0a1edcab6c241eb https://www.semanticscholar.org/paper/3861ae2a6bdd2a759c2d901a6583e63a216bc2fc https://www.semanticscholar.org/paper/4b71ab38d3ac6eee0da5e68ef666d33d1028bad8 https://www.semanticscholar.org/paper/05dc5fb3a3bdefdf181aafcc42cd80ff6b7704e7 https://www.semanticscholar.org/paper/cec3bffdb0968cd820863005af54cc519704c24a https://www.semanticscholar.org/paper/415f18130edbe06e3e4806dfb0a1edcab6c241eb