State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions . We propose a novel parameter estimation method for score normalization based on logistic regression , using the expected parameters from past queries . For each model , we present a structural operational semantics of PCCS and a notion of bisimulation equivalence which we prove to be a congruence . We also show that the models form a hierarchy : the reactive model is derivable from the generative model by abstraction from the relative probabilities of different actions , and the generative model is derivable from the stratified model by abstraction from the purely probabilistic branching structure . Moreover , the classical nonprobabilistic model is derivable from each of these models by abstraction from all probabilities . We observed that these games are reasonably balanced and have skill ele- ments , they are not yet entertaining for human players . The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality games This volume , edited by Terje Lohndal , is a Festschrift honoring the career and lasting contributions of Jan Terje Faarlund to the field of linguistics . The central theme connecting the various contributions housed in this volume involves the pursuit of a better understanding of the Faculty of Human Language , with the majority of the contributions adopting a generative perspective . The subsections of this volume reflect the rich and diverse research interests of Faarlund over the course of his + 40 year research career , ranging from topics such as Scandinavian syntax , Germanic sociolinguistics , Topics in French grammar , Language change , Lesser-studied languages , Language acquisition , and Language evolution . To date , most of these studies are either descriptive in nature or embedded within functional-cognitive theories . In contrast , we take a generative perspective on sign language grammaticalization , adopting ideas by Roberts & Roussou ( 2003 ) , who suggest that grammaticalization can be characterized as “ reanalysis ‘ upwards ’ along the functional structure ” . Following an overview of some of the attested modality-independent pathways , we zoom in on the grammaticalization of two types of agreement auxiliaries , the lexical sources of which are the verb GIVE and the noun PERSON . Syntactic treebanks , which as much as possible account for the language we wish to model , serve as the basis for defining the probabilistic parameters of such grammars . First , we exhibit in this thesis some drawbacks of the usual learning paradigms , due to the use of arbitrary heuristics ( STSG DOP model ) , or to the use of learning criteria that consider these grammars as generative ones ( creation of sentences from the grammar ) rather than dedicated to analysis ( creation of analyses from the sentence ) . In a second time , we propose new methods for training grammars , based on the traditional Maximum Entropy and Maximum Likelihood criteria . Problem-based Learning(PBL ) encourages and supports learners to explore actively , to resolve problems collaborately , to develop integrated qualities of inter-personal communication , with emphasis on the formation process of knowledge and skills . After adjustment , development and improvement , its implementation models have grown from an ideal , linear model into more feasible , generative ones , which focus more on students ' cooperation and self-oriented learning , as well as tutor 's effective guidance and the circularity and reflectivity of PBL process . We implemented our approach as the D-PRISM language by modifying PRISM , a logic-based probabilistic modeling language for generative modeling , while exploiting its dynamic programming mechanism for efficient probability computation . We tested D-PRISM with logistic regression , a linear-chain CRF and a CRF-CFG and empirically confirmed their excellent discriminative performance compared to their generative counterparts , i.e.\ naive Bayes , an HMM and a PCFG . We also introduced new CRF models , CRF-BNCs and CRF-LCGs . They are CRF versions of Bayesian network classifiers and probabilistic left-corner grammars respectively and easily implementable in D-PRISM . We empirically showed that they outperform their generative counterparts as expected . We incorporate weight information to improve the performance of face recognition with age variation which utilizes neural network and random decision forest to encode age variation . This age invariant recognition technique is classified into two categories-Discriminative based techniques and Generative based technique . Generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks . But these models can handle only conversations aligned with their previous written answers and , therefore , conversations can sometimes sound artificial if it goes out of the topic . Generative-based models can handle better an open conversation which makes them a more generalizable approach . Promising results have been achieved in generative-based models by applying neural machine translation techniques with the recurrent encoder/decoder architecture . Their popularity stems from their ability to use unlabelled data effectively , to incorporate abundant linguistic features , and to learn interpretable dependencies among data . However these successes are tempered by the fact that as the structure of such generative models becomes deeper and more complex , true Bayesian inference becomes intractable due to the high dimensional integrals required . Markov chain Monte Carlo subsection : Neural Variational Document Model ( 1 ) Inference Network : ( 2 ) Generative Model : ( 3 ) KL Divergence : The variational lower bound to be optimised : subsection : Neural Answer Selection Model This results in that the GANs learn to synthesize pairs of corresponding images without correspondence supervision . Generative Models : Let and be images drawn from the marginal distribution of the 1st domain , and the marginal distribution of the 2nd domain , , respectively . Let and be the generative models of and , which map a random vector input to images that have the same support as and , respectively . In this paper , we propose a novel solution , termed the Generative Partition Network ( GPN ) , to overcome essential limitations of the above two types of approaches and meanwhile inherit their strengths within a unified model for efficiently and effectively estimating poses of multiple persons in a given image . As shown in Figure [ reference ] , GPN solves multi - person pose estimation problem by simultaneously 1 ) modeling person detection for joint partition as a generative process inferred from all joint candidates and 2 ) performing local inference for obtaining joint categorizations and associations conditioned on the generated person detections . In particular , GPN introduces a dense regression module to generate person detections with partitioned joints via votes from joint candidates in a carefully designed embedding space , which is efficiently parameterized by person centroids . This paper describes the instrument design , provides an overview of the ground testing and calibration of the instrument and a summary of the sensor performance in space . In particular , the spatial imaging performance of ALI is discussed . Sample images are shown that demonstrate the improved capability of the sensor in terms of Pan band resolution and signal-to-noise ratio in all bands . Hyperion , a hyperspectral sensor , and the Advanced Land Imager ( ALI ) are carried on NASA 's EO-1 satellite . The Evaluation and Validation of EO-1 for Sustainable Development ( EVEOSD ) is our project supporting the EO-1 mission . To solve the transfer equation , we adopt the short characteristic method and examine different acceleration methods to obtain the source function . We use the ALI method and test local and non-local operators . Furthermore , we compare the Ng and the orthomin methods of acceleration The four basic subtasks , namely feature extraction , feature integration , attention building and attention reinforcement in our proposal of DSVA are described in detail by inferential CommonKADS schemes . It is shown that the ALI method , in its various forms , that is to say , recurrent and non-recurrent , temporal , spatial and spatial-temporal , may perfectly be used as a problem-solving-method in most of the subtasks involved in the DSVA task . q 2005 Elsevier Ltd. All rights reserved . Social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation . The latter are not well represented on the Web , commonly referred to as under-resourced languages , and the current available ALI tools fail to properly recognize them . In this paper , we revisit the problem of ALI with the focus on Arabicized Berber and dialectal Arabic short texts . The use of social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation . These new written languages are underresourced , hence the current ALI tools fail to properly recognize them . In this study , we revisit the problem of ALI with the focus on discriminating under-resourced similar languages . Currently foreseen automatic location identification ( ALI ) technology has allowed the FCC to adopt a requirement that service providers allow ALI within 125 m for a certain fraction of calls . We must take into account the fact that that is a large area ; emergency units responding to a call may not immediately find the emergency due to intervening obstacles . The transformation algorithm takes into account spectral responses of both the synthesized , broad , multispectral bands and the utilized , narrow , hyperspectral bands . To validate the spectral synthesis algorithm , simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments . Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations . This paper presents the results from multiple vicarious calibration experiments at a variety of sites for two of the Earth Observing 1 ( EO-1 ) optical sensors . The Advanced Land Imager ( ALI ) and Hyperion sensors operate in the visible and shortwave infrared portions of the spectrum . The ground sample distance of about 30 m works well for vicarious calibration and allows an easy comparison to legacy sensors such as the Enhanced Thematic Mapper Plus ( ETM+ ) which has the same 30-m ground sample distance . We demonstrate the advantages of NPBin in terms of interpretability of the estimated density and the accuracy in ALI detection using simulations and analysis of several ChIP‐seq data sets . We also illustrate the generality of our modeling framework beyond ALI detection by an application to a baseball batting average prediction problem . This article has supplementary material available at Biostatistics online . The Advanced Land Imager ( ALI ) was launched in 2000 on the EO-1 ( Earth Observer-1 ) satellite to test technology that will be used for the next Landsat platform , Landsat 8 . In comparison to Landsat 7 ETM+ , EO1 ALI provides a greater signal to noise ratio , a pushbroom sensor , greater quantization , and additional wavelength bands . As technology evolved , newer Landsat sensors were modified slightly while keeping in mind the importance of historical data continuity . The basic conclusion of this study is that the four sensors can provide excellent data continuity for temporal studies of natural resources . Furthermore , the new technologies put forward by the EO-1 ALI sensor have had no apparent effect on data continuity and should be considered for the upcoming Landsat-8 sensor payload . Significantly , the ALI provides the physical – virtual feedback loop , which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems . This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems . A case study was presented to initially validate the IoT-based ALI framework . In particular , among the compared semi - supervised algorithms , the improved GAN had recorded the best performance in literature . Furthermore , we also compared with the ALI that extended the classic GAN by jointly generating data and inferring their representations , which achieved comparable performance to the Improved GAN . This pointed out an interesting direction to extend the CLS - GAN by directly inferring the data representation , and we will leave it in the future work . We theoretically and empirically explore the explainability benefits of adversarial learning in logistic regression models on structured datasets . In particular we focus on improved explainability due to significantly higher $ \textit{feature-concentration}$ in adversarially-learned models : Compared to natural training , adversarial training tends to more efficiently shrink the weights of non-predictive and weakly-predictive features , while model performance on natural test data only degrades slightly ( and even sometimes improves ) , compared to that of a naturally trained model . We provide theoretical insight into this phenomenon via an analysis of the expectation of the logistic model weight updates by an SGD-based adversarial learning algorithm , where examples are drawn from a random binary data-generation process . Over the recent years , various research has been conducted to investigate methods for verifying users with a short randomized pass-phrase due to the increasing demand for voice-based authentication systems . In this paper , we propose a novel technique for extracting an i-vector-like feature based on an adversarially learned inference ( ALI ) model which summarizes the variability within the Gaussian mixture model ( GMM ) distribution through a nonlinear process . Analogous to the previously proposed variational autoencoder (VAE)-based feature extractor , the proposed ALI-based model is trained to generate the GMM supervector according to the maximum likelihood criterion given the Baum – Welch statistics of the input utterance . We introduce the adversarially learned inference ( ALI ) model , which jointly learns a generation network and an inference network using an adversarial process . The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables . This , hence , creates a robustness issue across emotional scenarios . In this work , we propose to learn an enhanced acoustic code vector for in-context emotion database through adversarially learning from large out-of-context emotion corpus to obtain robust emotion recognition . We demonstrate that our framework can obtain improved recognition accuracy using low dimensional representations on two different databases , and it maintains its modeling power even when given very limited in-context training samples . A long line of research has studied the power of conflict-driven clause learning ( CDCL ) and how it compares to the resolution proof system in which it searches for proofs . It has been shown that CDCL can polynomially simulate resolution even with an adversarially chosen learning scheme as long as it is asserting . However , the simulation only works under the assumption that no learned clauses are ever forgotten , and the polynomial blow-up is significant . A text-independent speaker verification system suffers severe performance degradation under short utterance condition . To address the problem , in this paper , we propose an adversarially learned embedding mapping model that directly maps a short embedding to an enhanced embedding with increased discriminability . In particular , a Wasserstein GAN with a bunch of loss criteria are investigated . However , unlike variational autoencoders ( VAEs ) , GANs lack encoders that provide the inverse mapping for the generators , i.e. , encode images back to the latent space . In this work , we consider adversarially learned generative models that also have encoders . We evaluate models based on their ability to produce high quality samples and reconstructions of real images . Several papers have proposed GAN-like algorithms for inference , however , connections to the theory of VI are not always well understood . This paper provides a unifying review of existing algorithms establishing connections between variational autoencoders , adversarially learned inference , operator VI , GAN-based image reconstruction , and more . Secondly , the paper provides a framework for building new algorithms : depending on the way the variational bound is expressed we introduce prior-contrastive and joint-contrastive methods , and show practical inference algorithms based on either density ratio estimation or denoising . We explore the application of Generative Adversarial Networks to the domain of morphological profiling of human cultured cells imaged by fluorescence microscopy . When evaluated for their ability to group cell images responding to treatment by chemicals of known classes , we find that adversarially learned representations are superior to autoencoder-based approaches . While currently inferior to classical computer vision and transfer learning , the adversarial framework enables useful visualization of the variation of cellular images due to their generative capabilities . Recent work in unsupervised image-to-image translation by adversarially learning mapping between different domains , which can not distinguish the foreground and background . The existing methods of image-to-image translation mainly transfer the global image across the source and target domains . WITH APPLICATION TO AMORTIZED MLE FOR GENERATIVE ADVERSAR - IAL LEARNING section : ABSTRACT We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity than previous work . Our proposed network , Dual Video Discriminator GAN ( DVD-GAN ) , scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator . We evaluate on the related tasks of video synthesis and video prediction , and achieve new state of the art Frechet Inception Distance on prediction for Kinetics-600 , as well as state of the art Inception Score for synthesis on the UCF-101 dataset , alongside establishing a number of strong baselines on Kinetics-600 . We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work . Our proposed model , Dual Video Discriminator GAN ( DVD-GAN ) , scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator . We evaluate on the related tasks of video synthesis and video prediction , and achieve new state-of-the-art Frechet Inception Distance for prediction for Kinetics-600 , as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset , alongside establishing a strong baseline for synthesis on Kinetics-600 . The performance on this task , which we call Classification Accuracy Score ( CAS ) , reveals some surprising results not identified by traditional metrics and constitute our contributions . First , when using a state-of-the-art GAN ( BigGAN-deep ) , Top-1 and Top-5 accuracy decrease by 27.9\% and 41.6\% , respectively , compared to the original data ; and conditional generative models from other model classes , such as Vector-Quantized Variational Autoencoder-2 ( VQ-VAE-2 ) and Hierarchical Autoregressive Models ( HAMs ) , substantially outperform GANs on this benchmark . Second , CAS automatically surfaces particular classes for which generative models failed to capture the data distribution , and were previously unknown in the literature . Our experiments demonstrate that latent optimisation can significantly improve GAN training , obtaining state-of-the-art performance for the ImageNet ( 128 x 128 ) dataset . Our model achieves an Inception Score ( IS ) of 148 and an Frechet Inception Distance ( FID ) of 3.4 , an improvement of 17 % and 32 % in IS and FID respectively , compared with the baseline BigGAN-deep model with the same architecture and number of parameters .
State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions . We propose a novel parameter estimation method for score normalization based on logistic regression , using the expected parameters from past queries .
For each model , we present a structural operational semantics of PCCS and a notion of bisimulation equivalence which we prove to be a congruence . We also show that the models form a hierarchy : the reactive model is derivable from the generative model by abstraction from the relative probabilities of different actions , and the generative model is derivable from the stratified model by abstraction from the purely probabilistic branching structure . Moreover , the classical nonprobabilistic model is derivable from each of these models by abstraction from all probabilities .
We observed that these games are reasonably balanced and have skill ele- ments , they are not yet entertaining for human players . The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality games
This volume , edited by Terje Lohndal , is a Festschrift honoring the career and lasting contributions of Jan Terje Faarlund to the field of linguistics . The central theme connecting the various contributions housed in this volume involves the pursuit of a better understanding of the Faculty of Human Language , with the majority of the contributions adopting a generative perspective . The subsections of this volume reflect the rich and diverse research interests of Faarlund over the course of his + 40 year research career , ranging from topics such as Scandinavian syntax , Germanic sociolinguistics , Topics in French grammar , Language change , Lesser-studied languages , Language acquisition , and Language evolution .
To date , most of these studies are either descriptive in nature or embedded within functional-cognitive theories . In contrast , we take a generative perspective on sign language grammaticalization , adopting ideas by Roberts & Roussou ( 2003 ) , who suggest that grammaticalization can be characterized as “ reanalysis ‘ upwards ’ along the functional structure ” . Following an overview of some of the attested modality-independent pathways , we zoom in on the grammaticalization of two types of agreement auxiliaries , the lexical sources of which are the verb GIVE and the noun PERSON .
Syntactic treebanks , which as much as possible account for the language we wish to model , serve as the basis for defining the probabilistic parameters of such grammars . First , we exhibit in this thesis some drawbacks of the usual learning paradigms , due to the use of arbitrary heuristics ( STSG DOP model ) , or to the use of learning criteria that consider these grammars as generative ones ( creation of sentences from the grammar ) rather than dedicated to analysis ( creation of analyses from the sentence ) . In a second time , we propose new methods for training grammars , based on the traditional Maximum Entropy and Maximum Likelihood criteria .
Problem-based Learning(PBL ) encourages and supports learners to explore actively , to resolve problems collaborately , to develop integrated qualities of inter-personal communication , with emphasis on the formation process of knowledge and skills . After adjustment , development and improvement , its implementation models have grown from an ideal , linear model into more feasible , generative ones , which focus more on students ' cooperation and self-oriented learning , as well as tutor 's effective guidance and the circularity and reflectivity of PBL process .
We implemented our approach as the D-PRISM language by modifying PRISM , a logic-based probabilistic modeling language for generative modeling , while exploiting its dynamic programming mechanism for efficient probability computation . We tested D-PRISM with logistic regression , a linear-chain CRF and a CRF-CFG and empirically confirmed their excellent discriminative performance compared to their generative counterparts , i.e.\ naive Bayes , an HMM and a PCFG . We also introduced new CRF models , CRF-BNCs and CRF-LCGs . They are CRF versions of Bayesian network classifiers and probabilistic left-corner grammars respectively and easily implementable in D-PRISM . We empirically showed that they outperform their generative counterparts as expected .
We incorporate weight information to improve the performance of face recognition with age variation which utilizes neural network and random decision forest to encode age variation . This age invariant recognition technique is classified into two categories-Discriminative based techniques and Generative based technique . Generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks .
But these models can handle only conversations aligned with their previous written answers and , therefore , conversations can sometimes sound artificial if it goes out of the topic . Generative-based models can handle better an open conversation which makes them a more generalizable approach . Promising results have been achieved in generative-based models by applying neural machine translation techniques with the recurrent encoder/decoder architecture .
Their popularity stems from their ability to use unlabelled data effectively , to incorporate abundant linguistic features , and to learn interpretable dependencies among data . However these successes are tempered by the fact that as the structure of such generative models becomes deeper and more complex , true Bayesian inference becomes intractable due to the high dimensional integrals required . Markov chain Monte Carlo
subsection : Neural Variational Document Model ( 1 ) Inference Network : ( 2 ) Generative Model : ( 3 ) KL Divergence : The variational lower bound to be optimised : subsection : Neural Answer Selection Model
This results in that the GANs learn to synthesize pairs of corresponding images without correspondence supervision . Generative Models : Let and be images drawn from the marginal distribution of the 1st domain , and the marginal distribution of the 2nd domain , , respectively . Let and be the generative models of and , which map a random vector input to images that have the same support as and , respectively .
In this paper , we propose a novel solution , termed the Generative Partition Network ( GPN ) , to overcome essential limitations of the above two types of approaches and meanwhile inherit their strengths within a unified model for efficiently and effectively estimating poses of multiple persons in a given image . As shown in Figure [ reference ] , GPN solves multi - person pose estimation problem by simultaneously 1 ) modeling person detection for joint partition as a generative process inferred from all joint candidates and 2 ) performing local inference for obtaining joint categorizations and associations conditioned on the generated person detections . In particular , GPN introduces a dense regression module to generate person detections with partitioned joints via votes from joint candidates in a carefully designed embedding space , which is efficiently parameterized by person centroids .
This paper describes the instrument design , provides an overview of the ground testing and calibration of the instrument and a summary of the sensor performance in space . In particular , the spatial imaging performance of ALI is discussed . Sample images are shown that demonstrate the improved capability of the sensor in terms of Pan band resolution and signal-to-noise ratio in all bands .
Hyperion , a hyperspectral sensor , and the Advanced Land Imager ( ALI ) are carried on NASA 's EO-1 satellite . The Evaluation and Validation of EO-1 for Sustainable Development ( EVEOSD ) is our project supporting the EO-1 mission .
To solve the transfer equation , we adopt the short characteristic method and examine different acceleration methods to obtain the source function . We use the ALI method and test local and non-local operators . Furthermore , we compare the Ng and the orthomin methods of acceleration
The four basic subtasks , namely feature extraction , feature integration , attention building and attention reinforcement in our proposal of DSVA are described in detail by inferential CommonKADS schemes . It is shown that the ALI method , in its various forms , that is to say , recurrent and non-recurrent , temporal , spatial and spatial-temporal , may perfectly be used as a problem-solving-method in most of the subtasks involved in the DSVA task . q 2005 Elsevier Ltd. All rights reserved .
Social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation . The latter are not well represented on the Web , commonly referred to as under-resourced languages , and the current available ALI tools fail to properly recognize them . In this paper , we revisit the problem of ALI with the focus on Arabicized Berber and dialectal Arabic short texts .
The use of social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation . These new written languages are underresourced , hence the current ALI tools fail to properly recognize them . In this study , we revisit the problem of ALI with the focus on discriminating under-resourced similar languages .
Currently foreseen automatic location identification ( ALI ) technology has allowed the FCC to adopt a requirement that service providers allow ALI within 125 m for a certain fraction of calls . We must take into account the fact that that is a large area ; emergency units responding to a call may not immediately find the emergency due to intervening obstacles .
The transformation algorithm takes into account spectral responses of both the synthesized , broad , multispectral bands and the utilized , narrow , hyperspectral bands . To validate the spectral synthesis algorithm , simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments . Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations .
This paper presents the results from multiple vicarious calibration experiments at a variety of sites for two of the Earth Observing 1 ( EO-1 ) optical sensors . The Advanced Land Imager ( ALI ) and Hyperion sensors operate in the visible and shortwave infrared portions of the spectrum . The ground sample distance of about 30 m works well for vicarious calibration and allows an easy comparison to legacy sensors such as the Enhanced Thematic Mapper Plus ( ETM+ ) which has the same 30-m ground sample distance .
We demonstrate the advantages of NPBin in terms of interpretability of the estimated density and the accuracy in ALI detection using simulations and analysis of several ChIP‐seq data sets . We also illustrate the generality of our modeling framework beyond ALI detection by an application to a baseball batting average prediction problem . This article has supplementary material available at Biostatistics online .
The Advanced Land Imager ( ALI ) was launched in 2000 on the EO-1 ( Earth Observer-1 ) satellite to test technology that will be used for the next Landsat platform , Landsat 8 . In comparison to Landsat 7 ETM+ , EO1 ALI provides a greater signal to noise ratio , a pushbroom sensor , greater quantization , and additional wavelength bands . As technology evolved , newer Landsat sensors were modified slightly while keeping in mind the importance of historical data continuity .
The basic conclusion of this study is that the four sensors can provide excellent data continuity for temporal studies of natural resources . Furthermore , the new technologies put forward by the EO-1 ALI sensor have had no apparent effect on data continuity and should be considered for the upcoming Landsat-8 sensor payload .
Significantly , the ALI provides the physical – virtual feedback loop , which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems . This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems . A case study was presented to initially validate the IoT-based ALI framework .
In particular , among the compared semi - supervised algorithms , the improved GAN had recorded the best performance in literature . Furthermore , we also compared with the ALI that extended the classic GAN by jointly generating data and inferring their representations , which achieved comparable performance to the Improved GAN . This pointed out an interesting direction to extend the CLS - GAN by directly inferring the data representation , and we will leave it in the future work .
We theoretically and empirically explore the explainability benefits of adversarial learning in logistic regression models on structured datasets . In particular we focus on improved explainability due to significantly higher $ \textit{feature-concentration}$ in adversarially-learned models : Compared to natural training , adversarial training tends to more efficiently shrink the weights of non-predictive and weakly-predictive features , while model performance on natural test data only degrades slightly ( and even sometimes improves ) , compared to that of a naturally trained model . We provide theoretical insight into this phenomenon via an analysis of the expectation of the logistic model weight updates by an SGD-based adversarial learning algorithm , where examples are drawn from a random binary data-generation process .
Over the recent years , various research has been conducted to investigate methods for verifying users with a short randomized pass-phrase due to the increasing demand for voice-based authentication systems . In this paper , we propose a novel technique for extracting an i-vector-like feature based on an adversarially learned inference ( ALI ) model which summarizes the variability within the Gaussian mixture model ( GMM ) distribution through a nonlinear process . Analogous to the previously proposed variational autoencoder (VAE)-based feature extractor , the proposed ALI-based model is trained to generate the GMM supervector according to the maximum likelihood criterion given the Baum – Welch statistics of the input utterance .
We introduce the adversarially learned inference ( ALI ) model , which jointly learns a generation network and an inference network using an adversarial process . The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables .
This , hence , creates a robustness issue across emotional scenarios . In this work , we propose to learn an enhanced acoustic code vector for in-context emotion database through adversarially learning from large out-of-context emotion corpus to obtain robust emotion recognition . We demonstrate that our framework can obtain improved recognition accuracy using low dimensional representations on two different databases , and it maintains its modeling power even when given very limited in-context training samples .
A long line of research has studied the power of conflict-driven clause learning ( CDCL ) and how it compares to the resolution proof system in which it searches for proofs . It has been shown that CDCL can polynomially simulate resolution even with an adversarially chosen learning scheme as long as it is asserting . However , the simulation only works under the assumption that no learned clauses are ever forgotten , and the polynomial blow-up is significant .
A text-independent speaker verification system suffers severe performance degradation under short utterance condition . To address the problem , in this paper , we propose an adversarially learned embedding mapping model that directly maps a short embedding to an enhanced embedding with increased discriminability . In particular , a Wasserstein GAN with a bunch of loss criteria are investigated .
However , unlike variational autoencoders ( VAEs ) , GANs lack encoders that provide the inverse mapping for the generators , i.e. , encode images back to the latent space . In this work , we consider adversarially learned generative models that also have encoders . We evaluate models based on their ability to produce high quality samples and reconstructions of real images .
Several papers have proposed GAN-like algorithms for inference , however , connections to the theory of VI are not always well understood . This paper provides a unifying review of existing algorithms establishing connections between variational autoencoders , adversarially learned inference , operator VI , GAN-based image reconstruction , and more . Secondly , the paper provides a framework for building new algorithms : depending on the way the variational bound is expressed we introduce prior-contrastive and joint-contrastive methods , and show practical inference algorithms based on either density ratio estimation or denoising .
We explore the application of Generative Adversarial Networks to the domain of morphological profiling of human cultured cells imaged by fluorescence microscopy . When evaluated for their ability to group cell images responding to treatment by chemicals of known classes , we find that adversarially learned representations are superior to autoencoder-based approaches . While currently inferior to classical computer vision and transfer learning , the adversarial framework enables useful visualization of the variation of cellular images due to their generative capabilities .
Recent work in unsupervised image-to-image translation by adversarially learning mapping between different domains , which can not distinguish the foreground and background . The existing methods of image-to-image translation mainly transfer the global image across the source and target domains .
WITH APPLICATION TO AMORTIZED MLE FOR GENERATIVE ADVERSAR - IAL LEARNING section : ABSTRACT
We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity than previous work . Our proposed network , Dual Video Discriminator GAN ( DVD-GAN ) , scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator . We evaluate on the related tasks of video synthesis and video prediction , and achieve new state of the art Frechet Inception Distance on prediction for Kinetics-600 , as well as state of the art Inception Score for synthesis on the UCF-101 dataset , alongside establishing a number of strong baselines on Kinetics-600 .
We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work . Our proposed model , Dual Video Discriminator GAN ( DVD-GAN ) , scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator . We evaluate on the related tasks of video synthesis and video prediction , and achieve new state-of-the-art Frechet Inception Distance for prediction for Kinetics-600 , as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset , alongside establishing a strong baseline for synthesis on Kinetics-600 .
The performance on this task , which we call Classification Accuracy Score ( CAS ) , reveals some surprising results not identified by traditional metrics and constitute our contributions . First , when using a state-of-the-art GAN ( BigGAN-deep ) , Top-1 and Top-5 accuracy decrease by 27.9\% and 41.6\% , respectively , compared to the original data ; and conditional generative models from other model classes , such as Vector-Quantized Variational Autoencoder-2 ( VQ-VAE-2 ) and Hierarchical Autoregressive Models ( HAMs ) , substantially outperform GANs on this benchmark . Second , CAS automatically surfaces particular classes for which generative models failed to capture the data distribution , and were previously unknown in the literature .
Our experiments demonstrate that latent optimisation can significantly improve GAN training , obtaining state-of-the-art performance for the ImageNet ( 128 x 128 ) dataset . Our model achieves an Inception Score ( IS ) of 148 and an Frechet Inception Distance ( FID ) of 3.4 , an improvement of 17 % and 32 % in IS and FID respectively , compared with the baseline BigGAN-deep model with the same architecture and number of parameters .
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ECIR FDG ICML NIPS ArXiv IGARSS VarDial@COLING IGARSS International Journal of Computer Vision ICASSP SAT ICASSP ArXiv NeurIPS
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