Given the inherent difficulty of training adversarial models , and low convergence speed of policy gradient , training GANs using policy gradient is a non-trivial problem and requires deep study . Currently GANs have used only differentiable discriminators for training . Policy-GAN germinates the possibility of using a wide variety of non-differentiable discriminator networks for training GANs , something which was not possible with the original GAN framework . This document defines a means of advertising one or more S-BFD Discriminators using the IS-IS Router Capability TLV . This document defines a new AVP for advertising one or more S-BFD Discriminators using the L2TPv3 Control Protocol AVP . However , few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion . In this work , we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence ( cohesion ) and paragraph ( coherence ) . Our model is trained using a simple yet efficient variant of policy gradient , called negative-critical sequence training , which is proposed to eliminate the need of training a separate critic for estimating baseline . Most significantly , it avoids many cases of asymptotically inefficient nested iteration through Cartesian products that occur in a straightforward stream-based implementation of multisets . It accomplishes this by employing two novel techniques : symbolic ( term ) representations of multisets , specifically for Cartesian products , for facilitating dynamic symbolic computation , which intersperses algebraic simplification steps with conventional data processing ; and discrimination-based joins , a generic technique for computing equijoins based on equivalence discriminators , as an alternative to hash-based and sort-merge joins . Full source code for GMP in Haskell , which is based on generic top-down discrimination ( not included ) , is included for experimentation . Despite an impressive performance from the latest GAN for generating hyper-realistic images , GAN discriminators have difficulty evaluating the quality of an individual generated sample . This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake . In general , speaker-dependent modeling techniques based on generative adversarial networks ( GANs ) improve synthesized speech quality . However , they are inadequate for multi-speaker training because conventional discriminators can not take into account speaker identity , which degrades anti-spoofing performance in GAN discriminators . We introduce two approaches as means to learn GAN speaker characteristics , i.e. , auxiliary features and tasks . Our proposed approach uses time and time – frequency features extracted from EMG signals . By employing two types of feature set for same class discriminators , we are able to select the best feature set-discriminator pairs . The decision made by each selected classifier is used to generate the final class for an input EMG signal through majority voting . Inspired by discrepancy measures and two-sample tests between probability distributions , we propose two such distributional adversaries that operate and predict on samples , and show how they can be easily implemented on top of existing models . Various experimental results show that generators trained with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with pointwise prediction discriminators . The application of our framework to domain adaptation also results in considerable improvement over recent state-of-the-art . Existing techniques do not demonstrate or fail to transfer high-frequency details in a global adversarial setting , or train a single local discriminator only to ensure image structure consistency and thus work only for relatively simple styles . Unlike others , our proposed local adversarial discriminators can distinguish whether the generated local image details are consistent with the corresponding regions in the given reference image in cross-image style transfer in an unsupervised setting . Incorporating these technical contributions , we achieve not only state-of-the-art results on conventional styles but also novel results involving complex and dramatic styles with high-frequency details covering large areas across multiple facial features . The Track discriminator adds 2.5ns to the peaking time in the first stage and can register signals between 1 and 10fC. The TR discriminator utilises the same basic configuration , but has a 10:1 attenuation at its input and adds 5ns to the shaping time to allow integration of the prompt and reflected wire chamber signal for accurate detection of the TR photons . The Track and TR discriminators switch separate 200μA current sources between shared differential outputs to form a current sum of the combined discriminator outputs . This ternary encoding scheme is based on the assumption that the Track discriminator output is always present when the TR discriminator is triggered due to its lower threshold . Specifically , we take the last - layer activation of the converged discriminator network as fixed pretrained feature , and build a linear classifier upon it . Hypothetically , if the discriminator does not degenerate , the extracted last - layer feature should maintain more information about the data points , especially compared to features from degenerated discriminators . Following this idea , we first train EGAN - Ent - NN , EGAN - Const , and GAN on the MNIST till convergence , and then extract the last - layer activation from their discriminator networks as fixed feature input . Then two discriminators can be connected to each of the predicted output for multi - level adversarial learning . We perform one - stage end - to - end training for the segmentation model and discriminators jointly , without using any prior knowledge of the data in the target domain . In the testing phase , we can simply discard discriminators and use the adapted segmentation model on target images , with no extra computational requirements . Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory . section : BOOSTING We can also consider taking the max over N discriminators as a form of boosting for the discriminator 's online classification problem ( online because G can produce an infinite data stream ) . The boosted discriminator is given a sample x t and must predict whether it came from the generator or the dataset . In case 1 , we assume access to the loss function at prediction time , which allows us to compute the max . It is possible to train the weak discriminators using boosting and then ignore the booster 's prediction by instead presenting max{V i } . We explore both variants in our experiments , using the adaptive algorithm proposed in [ reference ] . Unfortunately , boosting failed to produce promising results on the image generation tasks . Our goal is to learn a multimodal mapping between two visual domains and without paired training data . As illustrated in Figure [ reference ] , our framework consists of content encoders , attribute encoders , generators , and domain discriminators for both domains , and a content discriminators . Take domain as an example , the content encoder maps images onto a shared , domain - invariant content space ( ) and the attribute encoder maps images onto a domain - specific attribute space ( ) . In our setup , the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels . In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input , the SESAME discriminator is composed of two input streams that independently process the image and its semantics , using the latter to manipulate the results of the former . We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks : ( a ) image manipulation and ( b ) image generation conditioned on semantic labels . The method we have been using is based on our Self-Organizing Hierarchical Optimal Subspace Learning and Inference Framework ( SHOSLIF ) . It uses the theories of linear discriminant projection for automatic optimal feature selection in each of the internal nodes of a Space-Tessellation Tree . In this paper , we present our recent study on the applicability of the approach to variability in position , size , and 3D orientation . For effectively learning the discriminative features of data from different projects , we propose a Discriminative Adversarial Feature Learning ( DAFL ) approach for CSDP . DAFL consists of feature transformer and project discriminator , which compete with each other . A feature transformer tries to generate feature representation , which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data . Classifier approbation for the analysis of images that contains few thousands objects is realized . Projected classifier can be used for the analysis of different origin images that comprise bubble , elliptical , dumbbell shaped , stripe andramified elements . It is a discriminant function derived from the PCA . We discriminate an unknown test character during the recognition phase by projection and classification . The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character . Extensive experiments on UCI benchmark data sets , three facial image data sets and COREL color image data sets show the superior performance of i . Boosting over AdaBoost and other state-of-the-art projection-based classifiers . In detail , we first divide the samples into three types including noise sample , fuzzy sample and boundary sample . Then , we combine the projection discrimination with these boundary samples , thus proposing the regularization term which concerns the priori structural information of the boundary samples . By doing so , the classification ability of MatMHKS has been further improved . This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems . Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a projection distance classifier and a multiple layer perceptron neural network classifier , in both single and ensemble of classifier configurations . Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process . Photomultiplier noise is effectively eliminated by nanosecond coincidence circuitry . The tunnel diodes used in the coincidence arrangement also act as baseline or window discriminators . Counting efficiency for C14 solutions is 85 percent , limited mainly by light collection . An application of Gaussian mixture model(GMM ) can coarsely extract the foreground and reduce the region , then the system searching human objects only in the foreground area , which speeded up the detection velocity and reduced the false positives error rate in a practical application . Owing to the high dimension of histogram of oriented gradient(HOG ) feature vector , it proposed a novel descriptor which was called PCA-HOG.This descriptor used a classical dimension reduction method called principal component analysis(PCA ) to reduce the dimension of raw HOG.The novel descriptor had a lower dimension feature vector than that of HOG , so it gave a much faster speed of the detect window classification . The detect rate of PCA-HOG was almost as high as that of HOG.The experiment shows that the human object detection system combined with the PCA-HOG descriptor and Gaussian mixture model gives a remarkable performance of detection rate . One series of programs performs traditional voltage- and current-clamp experiments on macroscopic currents in whole cells . A second series interfaces with a hardware window discriminator to capture single-channel events in a recirculating buffer ; a stimulus can be triggered to fire during an open channel . Off-line programs perform standard analyses , and further processing can be performed with standard spreadsheet programs . However , the integration of automated person detection and RE-ID algorithms is not without problems , and the errors arising in this integration must be measured ( e.g. , detection failures that may hamper the RE-ID performance ) . In this paper , we present a window-based classifier based on a recently proposed architecture for the integration of pedestrian detectors and RE-ID algorithms , that takes the output of any bounding-box RE-ID classifier and exploits the temporal continuity of persons in video streams . We evaluate our contributions on a recently proposed dataset featuring 13 high-definition cameras and over 80 people , acquired during 30 min at rush hour in an office space scenario . Classifiers were induced from recordings of these subjects ' movements as they carried out repetitive finger tapping , a standard clinical assessment for Parkinson 's disease . For ease of interpretability , we used a relatively simple window-based classifier architecture which captures patterns that occur over a single tap cycle . Analysis of window matches suggested the importance of peak closing deceleration as a basis for classification . Since this data is extremely sensitive due to the presence of personal information , report de-identification is needed . We present two methods , a window classifier and an implementation of conditional random fields ( CRF ) in order to de-identify personal information of Spanish medical records provided by the MEDDOCAN challenge . CRF obtained the best results with a F1measure of 0.897 for named entity recognition with exact match ( subtask 1 ) , and 0.930 and 0.940 for inexact match ( subtask 2 strict and merged respectively ) . Its small size makes intraneural differential recording possible which may improve EMG noise rejection . The technique is based on a binary search clustering method and achieves higher classification success than a standard window discriminator .
Given the inherent difficulty of training adversarial models , and low convergence speed of policy gradient , training GANs using policy gradient is a non-trivial problem and requires deep study . Currently GANs have used only differentiable discriminators for training . Policy-GAN germinates the possibility of using a wide variety of non-differentiable discriminator networks for training GANs , something which was not possible with the original GAN framework .
This document defines a means of advertising one or more S-BFD Discriminators using the IS-IS Router Capability TLV .
This document defines a new AVP for advertising one or more S-BFD Discriminators using the L2TPv3 Control Protocol AVP .
However , few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion . In this work , we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence ( cohesion ) and paragraph ( coherence ) . Our model is trained using a simple yet efficient variant of policy gradient , called negative-critical sequence training , which is proposed to eliminate the need of training a separate critic for estimating baseline .
Most significantly , it avoids many cases of asymptotically inefficient nested iteration through Cartesian products that occur in a straightforward stream-based implementation of multisets . It accomplishes this by employing two novel techniques : symbolic ( term ) representations of multisets , specifically for Cartesian products , for facilitating dynamic symbolic computation , which intersperses algebraic simplification steps with conventional data processing ; and discrimination-based joins , a generic technique for computing equijoins based on equivalence discriminators , as an alternative to hash-based and sort-merge joins . Full source code for GMP in Haskell , which is based on generic top-down discrimination ( not included ) , is included for experimentation .
Despite an impressive performance from the latest GAN for generating hyper-realistic images , GAN discriminators have difficulty evaluating the quality of an individual generated sample . This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake .
In general , speaker-dependent modeling techniques based on generative adversarial networks ( GANs ) improve synthesized speech quality . However , they are inadequate for multi-speaker training because conventional discriminators can not take into account speaker identity , which degrades anti-spoofing performance in GAN discriminators . We introduce two approaches as means to learn GAN speaker characteristics , i.e. , auxiliary features and tasks .
Our proposed approach uses time and time – frequency features extracted from EMG signals . By employing two types of feature set for same class discriminators , we are able to select the best feature set-discriminator pairs . The decision made by each selected classifier is used to generate the final class for an input EMG signal through majority voting .
Inspired by discrepancy measures and two-sample tests between probability distributions , we propose two such distributional adversaries that operate and predict on samples , and show how they can be easily implemented on top of existing models . Various experimental results show that generators trained with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with pointwise prediction discriminators . The application of our framework to domain adaptation also results in considerable improvement over recent state-of-the-art .
Existing techniques do not demonstrate or fail to transfer high-frequency details in a global adversarial setting , or train a single local discriminator only to ensure image structure consistency and thus work only for relatively simple styles . Unlike others , our proposed local adversarial discriminators can distinguish whether the generated local image details are consistent with the corresponding regions in the given reference image in cross-image style transfer in an unsupervised setting . Incorporating these technical contributions , we achieve not only state-of-the-art results on conventional styles but also novel results involving complex and dramatic styles with high-frequency details covering large areas across multiple facial features .
The Track discriminator adds 2.5ns to the peaking time in the first stage and can register signals between 1 and 10fC. The TR discriminator utilises the same basic configuration , but has a 10:1 attenuation at its input and adds 5ns to the shaping time to allow integration of the prompt and reflected wire chamber signal for accurate detection of the TR photons . The Track and TR discriminators switch separate 200μA current sources between shared differential outputs to form a current sum of the combined discriminator outputs . This ternary encoding scheme is based on the assumption that the Track discriminator output is always present when the TR discriminator is triggered due to its lower threshold .
Specifically , we take the last - layer activation of the converged discriminator network as fixed pretrained feature , and build a linear classifier upon it . Hypothetically , if the discriminator does not degenerate , the extracted last - layer feature should maintain more information about the data points , especially compared to features from degenerated discriminators . Following this idea , we first train EGAN - Ent - NN , EGAN - Const , and GAN on the MNIST till convergence , and then extract the last - layer activation from their discriminator networks as fixed feature input .
Then two discriminators can be connected to each of the predicted output for multi - level adversarial learning . We perform one - stage end - to - end training for the segmentation model and discriminators jointly , without using any prior knowledge of the data in the target domain . In the testing phase , we can simply discard discriminators and use the adapted segmentation model on target images , with no extra computational requirements .
Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory .
section : BOOSTING We can also consider taking the max over N discriminators as a form of boosting for the discriminator 's online classification problem ( online because G can produce an infinite data stream ) . The boosted discriminator is given a sample x t and must predict whether it came from the generator or the dataset .
In case 1 , we assume access to the loss function at prediction time , which allows us to compute the max . It is possible to train the weak discriminators using boosting and then ignore the booster 's prediction by instead presenting max{V i } . We explore both variants in our experiments , using the adaptive algorithm proposed in [ reference ] . Unfortunately , boosting failed to produce promising results on the image generation tasks .
Our goal is to learn a multimodal mapping between two visual domains and without paired training data . As illustrated in Figure [ reference ] , our framework consists of content encoders , attribute encoders , generators , and domain discriminators for both domains , and a content discriminators . Take domain as an example , the content encoder maps images onto a shared , domain - invariant content space ( ) and the attribute encoder maps images onto a domain - specific attribute space ( ) .
In our setup , the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels . In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input , the SESAME discriminator is composed of two input streams that independently process the image and its semantics , using the latter to manipulate the results of the former . We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks : ( a ) image manipulation and ( b ) image generation conditioned on semantic labels .
The method we have been using is based on our Self-Organizing Hierarchical Optimal Subspace Learning and Inference Framework ( SHOSLIF ) . It uses the theories of linear discriminant projection for automatic optimal feature selection in each of the internal nodes of a Space-Tessellation Tree . In this paper , we present our recent study on the applicability of the approach to variability in position , size , and 3D orientation .
For effectively learning the discriminative features of data from different projects , we propose a Discriminative Adversarial Feature Learning ( DAFL ) approach for CSDP . DAFL consists of feature transformer and project discriminator , which compete with each other . A feature transformer tries to generate feature representation , which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data .
Classifier approbation for the analysis of images that contains few thousands objects is realized . Projected classifier can be used for the analysis of different origin images that comprise bubble , elliptical , dumbbell shaped , stripe andramified elements .
It is a discriminant function derived from the PCA . We discriminate an unknown test character during the recognition phase by projection and classification . The recognition is performed by projecting a test image onto the subspace defined by the dominant eigenvectors of each class and then choosing the class corresponding to the subspace with the minimum error as the class of the test character .
Extensive experiments on UCI benchmark data sets , three facial image data sets and COREL color image data sets show the superior performance of i . Boosting over AdaBoost and other state-of-the-art projection-based classifiers .
In detail , we first divide the samples into three types including noise sample , fuzzy sample and boundary sample . Then , we combine the projection discrimination with these boundary samples , thus proposing the regularization term which concerns the priori structural information of the boundary samples . By doing so , the classification ability of MatMHKS has been further improved .
This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems . Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a projection distance classifier and a multiple layer perceptron neural network classifier , in both single and ensemble of classifier configurations . Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process .
Photomultiplier noise is effectively eliminated by nanosecond coincidence circuitry . The tunnel diodes used in the coincidence arrangement also act as baseline or window discriminators . Counting efficiency for C14 solutions is 85 percent , limited mainly by light collection .
An application of Gaussian mixture model(GMM ) can coarsely extract the foreground and reduce the region , then the system searching human objects only in the foreground area , which speeded up the detection velocity and reduced the false positives error rate in a practical application . Owing to the high dimension of histogram of oriented gradient(HOG ) feature vector , it proposed a novel descriptor which was called PCA-HOG.This descriptor used a classical dimension reduction method called principal component analysis(PCA ) to reduce the dimension of raw HOG.The novel descriptor had a lower dimension feature vector than that of HOG , so it gave a much faster speed of the detect window classification . The detect rate of PCA-HOG was almost as high as that of HOG.The experiment shows that the human object detection system combined with the PCA-HOG descriptor and Gaussian mixture model gives a remarkable performance of detection rate .
One series of programs performs traditional voltage- and current-clamp experiments on macroscopic currents in whole cells . A second series interfaces with a hardware window discriminator to capture single-channel events in a recirculating buffer ; a stimulus can be triggered to fire during an open channel . Off-line programs perform standard analyses , and further processing can be performed with standard spreadsheet programs .
However , the integration of automated person detection and RE-ID algorithms is not without problems , and the errors arising in this integration must be measured ( e.g. , detection failures that may hamper the RE-ID performance ) . In this paper , we present a window-based classifier based on a recently proposed architecture for the integration of pedestrian detectors and RE-ID algorithms , that takes the output of any bounding-box RE-ID classifier and exploits the temporal continuity of persons in video streams . We evaluate our contributions on a recently proposed dataset featuring 13 high-definition cameras and over 80 people , acquired during 30 min at rush hour in an office space scenario .
Classifiers were induced from recordings of these subjects ' movements as they carried out repetitive finger tapping , a standard clinical assessment for Parkinson 's disease . For ease of interpretability , we used a relatively simple window-based classifier architecture which captures patterns that occur over a single tap cycle . Analysis of window matches suggested the importance of peak closing deceleration as a basis for classification .
Since this data is extremely sensitive due to the presence of personal information , report de-identification is needed . We present two methods , a window classifier and an implementation of conditional random fields ( CRF ) in order to de-identify personal information of Spanish medical records provided by the MEDDOCAN challenge . CRF obtained the best results with a F1measure of 0.897 for named entity recognition with exact match ( subtask 1 ) , and 0.930 and 0.940 for inexact match ( subtask 2 strict and merged respectively ) .
Its small size makes intraneural differential recording possible which may improve EMG noise rejection . The technique is based on a binary search clustering method and achieves higher classification success than a standard window discriminator .
PolicyGAN: Training generative adversarial networks using policy gradient Advertising S-BFD Discriminators in IS-IS Advertising S-BFD Discriminators in L2TPv3 Towards Coherent and Cohesive Long-form Text Generation Generic multiset programming with discrimination-based joins and symbolic Cartesian products Detecting GAN generated errors Multi-Speaker Modeling for DNN-based Speech Synthesis Incorporating Generative Adversarial Networks Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach Distributional Adversarial Networks LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup Implementation of the ASDBLR and DTMROC ASICS for the ATLAS TRT in DMILL technology Calibrating Energy-based Generative Adversarial Networks Learning to Adapt Structured Output Space for Semantic Segmentation CyCADA: Cycle-Consistent Adversarial Domain Adaptation Generative Multi-Adversarial Networks Diverse Image-to-Image Translation via Disentangled Representations SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects Discriminant analysis and eigenspace partition tree for face and object recognition from views Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction РАСПОЗНАВАНИЕ ОБЪЕКТОВ В ЛАБИРИНТНЫХ ДОМЕННЫХ СТРУКТУРАХ Gray-scale handwritten character recognition based on principal features Interactive Boosting for Image Classification Matrix-pattern-oriented classifier with boundary projection discrimination An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization A Liquid Scintillation Counter Using Anticoincidence Shielding Motion human detection based on mixture of Gaussians and PCA-HOG Software for electrophysiological experiments with a personal computer A Window-Based Classifier for Automatic Video-Based Reidentification Evolving classifiers to inform clinical assessment of Parkinson's disease Window Classifiers and Conditional Random Fields for Medical Report De-Identification A dual-channel intrafascicular electrode for tracking single cell activities in peripheral nerves
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ICAPR ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML ICLR ECCV FG Document Recognition MCAM IJCNN CICARE IberLEF@SEPLN
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