Fluorescence microscopy can provide extensive information about this , although photobleaching and phototoxicity frequently limit the ability to continuously observe changes in single cells over the many days that differentiation occurs . Here we describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points . We show that our spherical harmonic-based model can accurately represent cell and nuclear shapes by measuring reconstruction errors . We introduce the Pitman Yor Diffusion Tree ( PYDT ) for hierarchical clustering , a generalization of the Dirichlet Diffusion Tree ( Neal , 2001 ) which removes the restriction to binary branching structure . The generative process is described and shown to result in an exchangeable distribution over data points . We prove some theoretical properties of the model and then present two inference methods : a collapsed MCMC sampler which allows us to model uncertainty over tree structures , and a computationally efficient greedy Bayesian EM search algorithm . Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables . Seen from a generative perspective , the multiplicative interactions emulates the entangling of factors of variation . Inference in the model can be seen as disentangling these generative factors . The algorithm addresses the two issues discussed above as follows . First , while training the generative model we avoid the exposure bias by using model predictions at training time . Second , we directly optimize for our final evaluation metric . However , traditional DIR methods such as optical flow and demons are iterative and generally slow especially for large 4D-CT datasets . In this paper , we present our preliminary results on using a fast-unsupervised generative adversarial network ( GAN ) to generate deformation vector fields ( DVF ) for 4D-CT DIR to help motion management and treatment planning in radiation therapy . The proposed network was trained in an unsupervised fashion without the need of ground truth DVF or anatomical labels . To that end , we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term . Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss , the proposed steerable GAN ( sGAN ) method is evaluated on the large public database IXI . Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values , while it leads to improved visual perceptual quality . The intensive computation and memory requirements of generative adversarial neural networks ( GANs ) hinder its real-world deployment on edge devices such as smartphones . Despite the success in model reduction of CNNs , neural network quantization methods have not yet been studied on GANs , which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models . In this paper , we start with an extensive study on applying existing successful methods to quantize GANs . Remote sensing image classification is challenging due to low separation between different classes and difficulty in learning discriminative features . GAN ( Generative Adversarial Model ) is promising for this task due to the generator in reproducing samples and the discriminator for improving the generator . Among GANs variants for image translation and image classification tasks , Pix2Pix performs best . It is assumed here that the left-view ( LV ) image is under-exposed and the right-view ( RV ) image is overexposed . First , a view exposure transfer GAN ( VET-GAN ) is constructed to transfer exposure information of the RV image to the LV image to generate the multi-exposure LV images , and then an HDR fusion GAN is constructed to fuse the generated multi-exposure LV images into an LV HDR image . Similarly , an RV HDR image can be generated using the same way to form a stereo HDR image pair . Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator . Extensive evaluations on the challenging Tsinghua-Tencent 100 K [ 45 ] and the Caltech [ 9 ] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects , including traffic signs and pedestrians , over well-established state-of-the-arts . Moreover , as free-form masks may appear anywhere in images with any shape , global and local GANs designed for a single rectangular mask are not applicable . Thus , we also present a patch-based GAN loss , named SN-PatchGAN , by applying spectral-normalized discriminator on dense image patches . SN-PatchGAN is simple in formulation , fast and stable in training . The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) . Instead of trying to explicitly assign probability to every x in the data distribution , GAN learns a generator network G that generates samples from the generator distribution P G by transforming a noise variable z ∼ P noise ( z ) into a sample G ( z ) . Learning meaningful representations of data is an important step for models to understand the world . Recently , the Generative Adversarial Network ( GAN ) has been proposed as a method that can learn characteristics of data distributions without the need for labels . GANs traditionally consist of a generator , which generates data from randomly sampled vectors , and a discriminator , which tries to distinguish generated data from real data . A middle ground of directly connecting the highest convolutional features to the input and output respectively of the generator and discriminator worked well . The first layer of the GAN , which takes a uniform noise distribution as input , could be called fully connected as it is just a matrix multiplication , but the result is reshaped into a 4 - dimensional tensor and used as the start of the convolution stack . For the discriminator , the last convolution layer is flattened and then fed into a single sigmoid output . The generators in GANs can map from noise vectors to realistic images . Several recent efforts explore conditional GAN in various contexts including conditioned on text , low - resolution images , video frames , and image . Our work focuses on using GAN conditioned on an input image . In contrast to several existing conditional GAN frameworks that require paired training data , our model produces diverse outputs without paired data . This suggests that our method has wider applicability to problems where paired training datasets are scarce or not available . [ reference ] . This approach uses a conditional GAN and is representative of the dominant stream of research in image synthesis . The generator is an encoder - decoder However , it still needs to assume the discriminator has infinite modeling capacity to prove the result in a non - parametric fashion , and its generalizability of producing new data out of training examples is unknown without theoretical proof or empirical evidence . In addition , presented to analyze the GAN from information theoretical perspective , and they seek to minimize the variational estimate of f - divergence , and show that the classic GAN is included as a special case of f - GAN . In contrast , InfoGAN proposed another information - theoretic GAN to learn disentangled representations capturing various latent concepts and factors in generating samples . Note that our framework is generally applicable to the setting with multiple stacks , and we hypothesize that using more stacks would be helpful for large - scale and high - resolution datasets . For all datasets , our top GAN generates fc3 features from some random noise , conditioned on label . The bottom GAN generates images from some noise , conditioned on fc3 features generated from GAN . As shown in Tab . [ reference ] , SGAN obtains a score of , outperforming AC - GAN ( ) and Improved GAN ( ) . Also , note that the techniques introduced in are not used in our implementations . section : Implementation Improved GAN Training . To stabilize the training process and generate higher quality images , we replace Eq . The input codes for the generator are drawn from Uniform [ −1 , 1 ] 128 . We consider two parameterizations for each critic : a standard one where the parameters can take any real value , and a spectral parametrization ( denoted SN - ) as above [ reference ] . Models without explicit gradient control ( SN - GAN , SN - MMDGAN , SN - MMGAN - L2 , SN - WGAN ) Table [ reference ] shows the performance of previous state - of - the - art supervised and unsupervised methods in shape classification on both variants of the ModelNet dataset . Notably , the best unsupervised state - of - the - art results reported so far were from the 3D - GAN of , which used features from 3 layers of convolutional networks with total dimensions . This is a far larger feature space than that required by our model , which is simply ( for 10 - way classification ) and ( for 40 - way classification ) and reaches the exact same level of performance . Subsequently , the heatmap of smoke thickness based on HSV or gray feature is established , and the space – time distribution of the smoke region is analyzed . In addition , generative adversarial network is used to predict the future frames and smoke trend heatmap , which will contribute to the development of fire protection and provide suggestions for rescue or evacuation . The experimental results show that the proposed method can accurately detect the fire smoke in different scenes and provide an effective heatmap analysis scheme , as well as provides basic data for further study on the trend of fire . A generative adversarial network ( GAN ) based fully automatic electrocardiogram ( ECG ) arrhythmia classification system with high performance is presented in this paper . We have designed a discriminator ( D ) to take ECG coupling matrix as input , and then predict the input validity ( real or generated ) as well as arrhythmia classes . We introduce a new problem of gaze anticipation on future frames which extends the conventional gaze prediction problem to go beyond current frames . To solve this problem , we propose a new generative adversarial network based model , Deep Future Gaze ( DFG ) , encompassing two pathways : DFG-P is to anticipate gaze prior maps conditioned on the input frame which provides task influences ; DFG-G is to learn to model both semantic and motion information in future frame generation . DFG-P and DFG-G are then fused to anticipate future gazes . Deep learning based image-to-image translation methods aim at learning the joint distribution of the two domains and finding transformations between them . Despite recent GAN ( Generative Adversarial Network ) based methods have shown compelling results , they are prone to fail at preserving image-objects and maintaining translation consistency , which reduces their practicality on tasks such as generating large-scale training data for different domains . To address this problem , we purpose a structure-aware image-to-image translation network , which is composed of encoders , generators , discriminators and parsing nets for the two domains , respectively , in a unified framework . The results show that the generated pedestrian structures are indistinguishable from the ground truth pose structures when classified using a suitably trained classifier . The paper demonstrates that the Generative Adversarial Network architecture can be used to create realistic new training samples , and , in future , new pedestrian events . Then , we show two applications with introducing the proposed named entity hypersphere model . First , using a generative adversarial neural network to learn a transformation matrix of two embedding spaces , which results in a convenient determination of named entity distribution in the target language , indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces . Second , the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-the-art results . We propose a generative adversarial neural network model for relation classification that attempts to emulate the way in which human analysts might process sentences . Our approach provides two unique benefits over existing capabilities : ( 1 ) we make predictions by finding and exploiting supportive rationales to improve interpretability ( i.e. words or phrases extracted from a sentence that a person can reason upon ) , and ( 2 ) we allow predictions to be easily corrected by adjusting the rationales . In this paper , we suggest an approach for automatic dataset generation for text recognition for arbitrary languages . We use a generative adversarial network structure , which is adapted to generate readable and clear text looking naturally on the image background . We evaluate our approach using SegLink and Textboxes++ text localization models , which were trained on examples generated by SynthText and by variations of our method . The focus of this work is on the development of a Machine Learning algorithm , more precisely a Generative Adversarial Network , for the automated segmentation of the human Hippocampus and its substructures in Magnetic Resonance Images . In particular , the task is to determine if the integration of a pre-trained network that generates segmentations into a Generative Adversarial Network scheme can improve generated segmentations . In this context , a segmentation network in form of a U-net corresponds to the generator . Second , we parameterize the value and density functions by two neural networks , respectively . By phrasing the problem in this manner , solving the MFG can be interpreted as a special case of training a generative adversarial generative network ( GAN ) . We show the potential of our method on up to 50-dimensional MFG problems . However , social network data usually contain lots of missing values so that predicting accurate credit level is not feasible . We propose a neural network based approach and adapt Generative Adversarial Network concept to generate the missing values . The Generator could output the possible values of the missing values ; while the Discriminator could judge if the output values from Generator is appropriate or not for further training . Especially for the multi-classification problem , some conventional methods can not have good performance on dealing with unbalanced data . In this paper , a method based on generative adversarial network network which generates data for data unbalanced compensation is proposed . This method use designed generator to generate the virtual data which has significant useful features to puzzle the discriminator . For example , in the case of digit classification this may be the standard LeNet model . However , Liu and Tuzel achieve state of the art results on unsupervised MNIST - USPS using two generative adversarial networks . These generative models use random noise as input to generate samples in image space section : ABSTRACT In this paper we propose equipping Generative Adversarial Networks with the ability to produce direct energy estimates for samples . Specifically , we develop a flexible adversarial training framework , and prove this framework not only ensures the generator converges to the true data distribution , but also enables the discriminator to retain the density information at the global optimum . document : Stacked Generative Adversarial Networks In this paper , we propose a novel generative model named Stacked Generative Adversarial Networks ( SGAN ) , which is trained to invert the hierarchical representations of a bottom - up discriminative network . Some early efforts include Restricted Boltzmann Machines and Deep Belief Networks . More recently , several successful paradigms of deep generative models have emerged , including the auto - regressive models , Variational Auto - encoders ( VAEs ) , and Generative Adversarial Networks ( GANs ) . Our work builds upon the GAN framework , which employs a generator that transforms a noise vector into an image and a discriminator that distinguishes between real and generated images . This method requires a large number of faces of a single person for training , and can only generate faces with different pose and light . Gauthier developes a conditional generative adversarial network ( cGAN ) to generate facial image from a noise distribution and conditional attributes . Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model . Several recent efforts have been made to address these issues . Pix2pix applies conditional generative adversarial network to I2I translation problems . Nevertheless , the training process requires paired data . Ideally , a network should make the best use of its data statistics and possess enough representation power . We propose a 1 Similar to the Conditional Generative Adversarial Network [ reference ] . network with two prediction branches , one enjoys high flexibility in capturing complicated structures and the other exploits geometric continuity . Its representation power is further boosted by an hourglass structure . section : Introduction A classic Generative Adversarial Net ( GAN ) learns a discriminator and a generator by playing a two - player minimax game to generate samples from a data distribution . The discriminator is trained to distinguish real samples from those generated by the generator , and it in turn guides the generator to produce realistic samples that can fool the discriminator . [ reference ] . In this paper , we present a simple modification to the generative adversarial network objective that encourages it to learn interpretable and meaningful representations . We do so by maximizing the mutual information between a fixed small subset of the GAN 's noise variables and the observations , which turns out to be relatively straightforward . Goodfellow et al . [ reference ] introduced the Generative Adversarial Networks ( GAN ) , a framework for training deep generative models using a minimax game . The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) . G via the reparametrization trick . Hence L I ( G , Q ) can be added to GAN 's objectives with no change to GAN 's training procedure and we call the resulting algorithm Information Maximizing Generative Adversarial Networks ( InfoGAN ) . Eq [ reference ] shows that the lower bound becomes tight as the auxiliary distribution Q approaches the true posterior distribution : Our temporal ensembling differs from this by taking into account the evaluations over multiple epochs . Generative Adversarial Networks ( GAN ) have been recently used for semi - supervised learning with promising results [ reference ][ reference ][ reference ] . It Additive Gaussian noise σ = 0.15 conv1a 128 filters , 3 × 3 , pad = ' same ' , LReLU ( α = 0.1 ) conv1b 128 filters , 3 × 3 , pad = Importantly , our model formulation utilises visual and textual inputs during training but does not require that images be available at test time . We show that our latent variable MMT formulation improves considerably over strong baselines , including a multi-task learning approach ( Elliott and Kadar , 2017 ) and a conditional variational auto-encoder approach ( Toyama et al. , 2016 ) . Finally , we show improvements due to ( i ) predicting image features in addition to only conditioning on them , ( ii ) imposing a constraint on the minimum amount of information encoded in the latent variable , and ( iii ) by training on additional target-language image descriptions ( i.e. synthetic data ) . Image deraining is a critical yet highly challenging task , due to the fact that rain density varies across spatial locations , while the distribution patterns simultaneously vary across color channels . In this paper , we propose a variational image deraining ( VID ) method by formulating image deraining in a conditional variational auto-encoder framework . To achieve adaptive deraining to spatial rain density , we generate a density estimation map for each color channel , which can largely avoid over and under deraining . Abstract Variational Auto-Encoder ( VAE ) is an important probabilistic technology to model 1D vectorial data . However , when applying VAE model to 2D image , vectorization is necessary . We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence . Furthermore , we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space . In addition , we define a transfer signature for characterizing the negative transfer based on C-divergence , which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios . Our model aims to generate utterances that are diverse combinations of slot values and contexts that can appear together . To create various utterances containing a given condition , our deep generative model uses a conditional variational auto-encoder architecture . We conduct experiments on various slot filling datasets , specifically airline travel information systems ( ATIS ) , Snips , and MIT Corpus . Gauthier developes a conditional generative adversarial network ( cGAN ) to generate facial image from a noise distribution and conditional attributes . Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model . Oord et al . propose a conditional image generation model based on PixelCNN decoder for image generation conditioned on an arbitrary feature vector . It is fundamentally important to characterize the ambiguity of groundtruth for a given input , and practically desirable to be able to generate multiple predictions . Surprisingly , this goal can be achieved tactfully by simply using the min function as a wrapper to the above proposed loss , or by a conditional variational autoencoder . See Sec 4.4 . At the same time , the two approaches are orthogonal and can be combined , for example by feeding the output of convolutional DRAW into the recurrent network of Pixel RNN . We also report the likelihood for a ( non - recurrent ) variational auto - encoder and standard DRAW . For the variational auto - encoder we tested architectures with multiple layers , both deterministic and stochastic but with standard functional forms , and this was the best result that we obtained . Results We have developed novel deep generative models , constructed low-dimensional and generalizable representation of fold space , exploited sequence data with and without paired structures , and developed ultra-fast fold predictor as an oracle providing feedback . The resulting semi-supervised gcWGAN is assessed with the oracle over 100 novel folds not in the training set and found to generate more yields and cover 3.6 times more target folds compared to a competing data-driven method ( cVAE ) . Assessed with structure predictor over representative novel folds ( including one not even part of basis folds ) , gcWGAN designs are found to have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE . gcWGAN explores uncharted sequence space to design proteins by learning from current sequence-structure data .
Fluorescence microscopy can provide extensive information about this , although photobleaching and phototoxicity frequently limit the ability to continuously observe changes in single cells over the many days that differentiation occurs . Here we describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points . We show that our spherical harmonic-based model can accurately represent cell and nuclear shapes by measuring reconstruction errors .
We introduce the Pitman Yor Diffusion Tree ( PYDT ) for hierarchical clustering , a generalization of the Dirichlet Diffusion Tree ( Neal , 2001 ) which removes the restriction to binary branching structure . The generative process is described and shown to result in an exchangeable distribution over data points . We prove some theoretical properties of the model and then present two inference methods : a collapsed MCMC sampler which allows us to model uncertainty over tree structures , and a computationally efficient greedy Bayesian EM search algorithm .
Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables . Seen from a generative perspective , the multiplicative interactions emulates the entangling of factors of variation . Inference in the model can be seen as disentangling these generative factors .
The algorithm addresses the two issues discussed above as follows . First , while training the generative model we avoid the exposure bias by using model predictions at training time . Second , we directly optimize for our final evaluation metric .
However , traditional DIR methods such as optical flow and demons are iterative and generally slow especially for large 4D-CT datasets . In this paper , we present our preliminary results on using a fast-unsupervised generative adversarial network ( GAN ) to generate deformation vector fields ( DVF ) for 4D-CT DIR to help motion management and treatment planning in radiation therapy . The proposed network was trained in an unsupervised fashion without the need of ground truth DVF or anatomical labels .
To that end , we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term . Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss , the proposed steerable GAN ( sGAN ) method is evaluated on the large public database IXI . Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values , while it leads to improved visual perceptual quality .
The intensive computation and memory requirements of generative adversarial neural networks ( GANs ) hinder its real-world deployment on edge devices such as smartphones . Despite the success in model reduction of CNNs , neural network quantization methods have not yet been studied on GANs , which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models . In this paper , we start with an extensive study on applying existing successful methods to quantize GANs .
Remote sensing image classification is challenging due to low separation between different classes and difficulty in learning discriminative features . GAN ( Generative Adversarial Model ) is promising for this task due to the generator in reproducing samples and the discriminator for improving the generator . Among GANs variants for image translation and image classification tasks , Pix2Pix performs best .
It is assumed here that the left-view ( LV ) image is under-exposed and the right-view ( RV ) image is overexposed . First , a view exposure transfer GAN ( VET-GAN ) is constructed to transfer exposure information of the RV image to the LV image to generate the multi-exposure LV images , and then an HDR fusion GAN is constructed to fuse the generated multi-exposure LV images into an LV HDR image . Similarly , an RV HDR image can be generated using the same way to form a stereo HDR image pair .
Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator . Extensive evaluations on the challenging Tsinghua-Tencent 100 K [ 45 ] and the Caltech [ 9 ] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects , including traffic signs and pedestrians , over well-established state-of-the-arts .
Moreover , as free-form masks may appear anywhere in images with any shape , global and local GANs designed for a single rectangular mask are not applicable . Thus , we also present a patch-based GAN loss , named SN-PatchGAN , by applying spectral-normalized discriminator on dense image patches . SN-PatchGAN is simple in formulation , fast and stable in training .
The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) . Instead of trying to explicitly assign probability to every x in the data distribution , GAN learns a generator network G that generates samples from the generator distribution P G by transforming a noise variable z ∼ P noise ( z ) into a sample G ( z ) .
Learning meaningful representations of data is an important step for models to understand the world . Recently , the Generative Adversarial Network ( GAN ) has been proposed as a method that can learn characteristics of data distributions without the need for labels . GANs traditionally consist of a generator , which generates data from randomly sampled vectors , and a discriminator , which tries to distinguish generated data from real data .
A middle ground of directly connecting the highest convolutional features to the input and output respectively of the generator and discriminator worked well . The first layer of the GAN , which takes a uniform noise distribution as input , could be called fully connected as it is just a matrix multiplication , but the result is reshaped into a 4 - dimensional tensor and used as the start of the convolution stack . For the discriminator , the last convolution layer is flattened and then fed into a single sigmoid output .
The generators in GANs can map from noise vectors to realistic images . Several recent efforts explore conditional GAN in various contexts including conditioned on text , low - resolution images , video frames , and image . Our work focuses on using GAN conditioned on an input image . In contrast to several existing conditional GAN frameworks that require paired training data , our model produces diverse outputs without paired data . This suggests that our method has wider applicability to problems where paired training datasets are scarce or not available .
[ reference ] . This approach uses a conditional GAN and is representative of the dominant stream of research in image synthesis . The generator is an encoder - decoder
However , it still needs to assume the discriminator has infinite modeling capacity to prove the result in a non - parametric fashion , and its generalizability of producing new data out of training examples is unknown without theoretical proof or empirical evidence . In addition , presented to analyze the GAN from information theoretical perspective , and they seek to minimize the variational estimate of f - divergence , and show that the classic GAN is included as a special case of f - GAN . In contrast , InfoGAN proposed another information - theoretic GAN to learn disentangled representations capturing various latent concepts and factors in generating samples .
Note that our framework is generally applicable to the setting with multiple stacks , and we hypothesize that using more stacks would be helpful for large - scale and high - resolution datasets . For all datasets , our top GAN generates fc3 features from some random noise , conditioned on label . The bottom GAN generates images from some noise , conditioned on fc3 features generated from GAN .
As shown in Tab . [ reference ] , SGAN obtains a score of , outperforming AC - GAN ( ) and Improved GAN ( ) . Also , note that the techniques introduced in are not used in our implementations .
section : Implementation Improved GAN Training . To stabilize the training process and generate higher quality images , we replace Eq .
The input codes for the generator are drawn from Uniform [ −1 , 1 ] 128 . We consider two parameterizations for each critic : a standard one where the parameters can take any real value , and a spectral parametrization ( denoted SN - ) as above [ reference ] . Models without explicit gradient control ( SN - GAN , SN - MMDGAN , SN - MMGAN - L2 , SN - WGAN )
Table [ reference ] shows the performance of previous state - of - the - art supervised and unsupervised methods in shape classification on both variants of the ModelNet dataset . Notably , the best unsupervised state - of - the - art results reported so far were from the 3D - GAN of , which used features from 3 layers of convolutional networks with total dimensions . This is a far larger feature space than that required by our model , which is simply ( for 10 - way classification ) and ( for 40 - way classification ) and reaches the exact same level of performance .
Subsequently , the heatmap of smoke thickness based on HSV or gray feature is established , and the space – time distribution of the smoke region is analyzed . In addition , generative adversarial network is used to predict the future frames and smoke trend heatmap , which will contribute to the development of fire protection and provide suggestions for rescue or evacuation . The experimental results show that the proposed method can accurately detect the fire smoke in different scenes and provide an effective heatmap analysis scheme , as well as provides basic data for further study on the trend of fire .
A generative adversarial network ( GAN ) based fully automatic electrocardiogram ( ECG ) arrhythmia classification system with high performance is presented in this paper . We have designed a discriminator ( D ) to take ECG coupling matrix as input , and then predict the input validity ( real or generated ) as well as arrhythmia classes .
We introduce a new problem of gaze anticipation on future frames which extends the conventional gaze prediction problem to go beyond current frames . To solve this problem , we propose a new generative adversarial network based model , Deep Future Gaze ( DFG ) , encompassing two pathways : DFG-P is to anticipate gaze prior maps conditioned on the input frame which provides task influences ; DFG-G is to learn to model both semantic and motion information in future frame generation . DFG-P and DFG-G are then fused to anticipate future gazes .
Deep learning based image-to-image translation methods aim at learning the joint distribution of the two domains and finding transformations between them . Despite recent GAN ( Generative Adversarial Network ) based methods have shown compelling results , they are prone to fail at preserving image-objects and maintaining translation consistency , which reduces their practicality on tasks such as generating large-scale training data for different domains . To address this problem , we purpose a structure-aware image-to-image translation network , which is composed of encoders , generators , discriminators and parsing nets for the two domains , respectively , in a unified framework .
The results show that the generated pedestrian structures are indistinguishable from the ground truth pose structures when classified using a suitably trained classifier . The paper demonstrates that the Generative Adversarial Network architecture can be used to create realistic new training samples , and , in future , new pedestrian events .
Then , we show two applications with introducing the proposed named entity hypersphere model . First , using a generative adversarial neural network to learn a transformation matrix of two embedding spaces , which results in a convenient determination of named entity distribution in the target language , indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces . Second , the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-the-art results .
We propose a generative adversarial neural network model for relation classification that attempts to emulate the way in which human analysts might process sentences . Our approach provides two unique benefits over existing capabilities : ( 1 ) we make predictions by finding and exploiting supportive rationales to improve interpretability ( i.e. words or phrases extracted from a sentence that a person can reason upon ) , and ( 2 ) we allow predictions to be easily corrected by adjusting the rationales .
In this paper , we suggest an approach for automatic dataset generation for text recognition for arbitrary languages . We use a generative adversarial network structure , which is adapted to generate readable and clear text looking naturally on the image background . We evaluate our approach using SegLink and Textboxes++ text localization models , which were trained on examples generated by SynthText and by variations of our method .
The focus of this work is on the development of a Machine Learning algorithm , more precisely a Generative Adversarial Network , for the automated segmentation of the human Hippocampus and its substructures in Magnetic Resonance Images . In particular , the task is to determine if the integration of a pre-trained network that generates segmentations into a Generative Adversarial Network scheme can improve generated segmentations . In this context , a segmentation network in form of a U-net corresponds to the generator .
Second , we parameterize the value and density functions by two neural networks , respectively . By phrasing the problem in this manner , solving the MFG can be interpreted as a special case of training a generative adversarial generative network ( GAN ) . We show the potential of our method on up to 50-dimensional MFG problems .
However , social network data usually contain lots of missing values so that predicting accurate credit level is not feasible . We propose a neural network based approach and adapt Generative Adversarial Network concept to generate the missing values . The Generator could output the possible values of the missing values ; while the Discriminator could judge if the output values from Generator is appropriate or not for further training .
Especially for the multi-classification problem , some conventional methods can not have good performance on dealing with unbalanced data . In this paper , a method based on generative adversarial network network which generates data for data unbalanced compensation is proposed . This method use designed generator to generate the virtual data which has significant useful features to puzzle the discriminator .
For example , in the case of digit classification this may be the standard LeNet model . However , Liu and Tuzel achieve state of the art results on unsupervised MNIST - USPS using two generative adversarial networks . These generative models use random noise as input to generate samples in image space
section : ABSTRACT In this paper we propose equipping Generative Adversarial Networks with the ability to produce direct energy estimates for samples . Specifically , we develop a flexible adversarial training framework , and prove this framework not only ensures the generator converges to the true data distribution , but also enables the discriminator to retain the density information at the global optimum .
document : Stacked Generative Adversarial Networks In this paper , we propose a novel generative model named Stacked Generative Adversarial Networks ( SGAN ) , which is trained to invert the hierarchical representations of a bottom - up discriminative network .
Some early efforts include Restricted Boltzmann Machines and Deep Belief Networks . More recently , several successful paradigms of deep generative models have emerged , including the auto - regressive models , Variational Auto - encoders ( VAEs ) , and Generative Adversarial Networks ( GANs ) . Our work builds upon the GAN framework , which employs a generator that transforms a noise vector into an image and a discriminator that distinguishes between real and generated images .
This method requires a large number of faces of a single person for training , and can only generate faces with different pose and light . Gauthier developes a conditional generative adversarial network ( cGAN ) to generate facial image from a noise distribution and conditional attributes . Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model .
Several recent efforts have been made to address these issues . Pix2pix applies conditional generative adversarial network to I2I translation problems . Nevertheless , the training process requires paired data .
Ideally , a network should make the best use of its data statistics and possess enough representation power . We propose a 1 Similar to the Conditional Generative Adversarial Network [ reference ] . network with two prediction branches , one enjoys high flexibility in capturing complicated structures and the other exploits geometric continuity . Its representation power is further boosted by an hourglass structure .
section : Introduction A classic Generative Adversarial Net ( GAN ) learns a discriminator and a generator by playing a two - player minimax game to generate samples from a data distribution . The discriminator is trained to distinguish real samples from those generated by the generator , and it in turn guides the generator to produce realistic samples that can fool the discriminator .
[ reference ] . In this paper , we present a simple modification to the generative adversarial network objective that encourages it to learn interpretable and meaningful representations . We do so by maximizing the mutual information between a fixed small subset of the GAN 's noise variables and the observations , which turns out to be relatively straightforward .
Goodfellow et al . [ reference ] introduced the Generative Adversarial Networks ( GAN ) , a framework for training deep generative models using a minimax game . The goal is to learn a generator distribution P G ( x ) that matches the real data distribution P data ( x ) .
G via the reparametrization trick . Hence L I ( G , Q ) can be added to GAN 's objectives with no change to GAN 's training procedure and we call the resulting algorithm Information Maximizing Generative Adversarial Networks ( InfoGAN ) . Eq [ reference ] shows that the lower bound becomes tight as the auxiliary distribution Q approaches the true posterior distribution :
Our temporal ensembling differs from this by taking into account the evaluations over multiple epochs . Generative Adversarial Networks ( GAN ) have been recently used for semi - supervised learning with promising results [ reference ][ reference ][ reference ] . It Additive Gaussian noise σ = 0.15 conv1a 128 filters , 3 × 3 , pad = ' same ' , LReLU ( α = 0.1 ) conv1b 128 filters , 3 × 3 , pad =
Importantly , our model formulation utilises visual and textual inputs during training but does not require that images be available at test time . We show that our latent variable MMT formulation improves considerably over strong baselines , including a multi-task learning approach ( Elliott and Kadar , 2017 ) and a conditional variational auto-encoder approach ( Toyama et al. , 2016 ) . Finally , we show improvements due to ( i ) predicting image features in addition to only conditioning on them , ( ii ) imposing a constraint on the minimum amount of information encoded in the latent variable , and ( iii ) by training on additional target-language image descriptions ( i.e. synthetic data ) .
Image deraining is a critical yet highly challenging task , due to the fact that rain density varies across spatial locations , while the distribution patterns simultaneously vary across color channels . In this paper , we propose a variational image deraining ( VID ) method by formulating image deraining in a conditional variational auto-encoder framework . To achieve adaptive deraining to spatial rain density , we generate a density estimation map for each color channel , which can largely avoid over and under deraining .
Abstract Variational Auto-Encoder ( VAE ) is an important probabilistic technology to model 1D vectorial data . However , when applying VAE model to 2D image , vectorization is necessary .
We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence . Furthermore , we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space . In addition , we define a transfer signature for characterizing the negative transfer based on C-divergence , which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios .
Our model aims to generate utterances that are diverse combinations of slot values and contexts that can appear together . To create various utterances containing a given condition , our deep generative model uses a conditional variational auto-encoder architecture . We conduct experiments on various slot filling datasets , specifically airline travel information systems ( ATIS ) , Snips , and MIT Corpus .
Gauthier developes a conditional generative adversarial network ( cGAN ) to generate facial image from a noise distribution and conditional attributes . Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model . Oord et al . propose a conditional image generation model based on PixelCNN decoder for image generation conditioned on an arbitrary feature vector .
It is fundamentally important to characterize the ambiguity of groundtruth for a given input , and practically desirable to be able to generate multiple predictions . Surprisingly , this goal can be achieved tactfully by simply using the min function as a wrapper to the above proposed loss , or by a conditional variational autoencoder . See Sec 4.4 .
At the same time , the two approaches are orthogonal and can be combined , for example by feeding the output of convolutional DRAW into the recurrent network of Pixel RNN . We also report the likelihood for a ( non - recurrent ) variational auto - encoder and standard DRAW . For the variational auto - encoder we tested architectures with multiple layers , both deterministic and stochastic but with standard functional forms , and this was the best result that we obtained .
Results We have developed novel deep generative models , constructed low-dimensional and generalizable representation of fold space , exploited sequence data with and without paired structures , and developed ultra-fast fold predictor as an oracle providing feedback . The resulting semi-supervised gcWGAN is assessed with the oracle over 100 novel folds not in the training set and found to generate more yields and cover 3.6 times more target folds compared to a competing data-driven method ( cVAE ) . Assessed with structure predictor over representative novel folds ( including one not even part of basis folds ) , gcWGAN designs are found to have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE . gcWGAN explores uncharted sequence space to design proteins by learning from current sequence-structure data .
Image-derived Models of Cell Organization Changes During Differentiation of PC12 Cells Pitman-Yor Diffusion Trees Disentangling Factors of Variation via Generative Entangling Sequence Level Training with Recurrent Neural Networks 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI QGAN: Quantized Generative Adversarial Networks Enhancing Pix2Pix for Remote Sensing Image Classification New Stereo High Dynamic Range Imaging Method Using Generative Adversarial Networks Perceptual Generative Adversarial Networks for Small Object Detection Free-Form Image Inpainting With Gated Convolution InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Inferencing based on unsupervised learning of disentangled representations Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Diverse Image-to-Image Translation via Disentangled Representations Photographic Image Synthesis with Cascaded Refinement Networks Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Stacked Generative Adversarial Networks StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation On gradient regularizers for MMD GANs Learning a Hierarchical Latent-Variable Model of 3D Shapes Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier Anticipating Where People will Look Using Adversarial Networks AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation Generation of Pedestrian Pose Structures using Generative Adversarial Networks Modeling Named Entity Embedding Distribution into Hypersphere An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences Synthetic dataset generation for text recognition with generative adversarial networks Generative adversarial networks for automated hippocampus segmentation : development of an artificial neural network and integration in a generative adversarial network scheme to improve the segmentation of the hippocampus and its substructures APAC-Net: Alternating the Population and Agent Control via Two Neural Networks to Solve High-Dimensional Stochastic Mean Field Games AI-Based Online P2P Lending Risk Assessment On Social Network Data With Missing Value Generative Adversarial Network Based Multi-class Imbalanced Fault Diagnosis of Rolling Bearing Adversarial Discriminative Domain Adaptation Calibrating Energy-based Generative Adversarial Networks Stacked Generative Adversarial Networks Deep Identity-aware Transfer of Facial Attributes Diverse Image-to-Image Translation via Disentangled Representations A Point Set Generation Network for 3D Object Reconstruction from a Single Image Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Temporal Ensembling for Semi-Supervised Learning Latent Variable Model for Multi-modal Translation Variational Image Deraining Matrix-variate variational auto-encoder with applications to image process Continuous Transfer Learning with Label-informed Distribution Alignment Utterance Generation With Variational Auto-Encoder for Slot Filling in Spoken Language Understanding Deep Identity-aware Transfer of Facial Attributes A Point Set Generation Network for 3D Object Reconstruction from a Single Image Towards Conceptual Compression De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN)
250e5b30efe46032ddc290374ffa32de74d7fb30 57156f73746d295f5ed73948c8a43dd024048392 63cbac5a39cd926a806f60116b845f9bd70f5544 cd7bc01b27aa97d1d1504f77ec04fe9cecc4e21e a2a7e4fa70b2a81ce66bc8d76e0b41e0d98b417e 86d0818ddcc9a670f25026ee1012d850289d87e1 f87c9348e466681075044b629c71b738c205e12c c5d2edd9f746c4e0eadfb33e06fc353eb2fa8887 508eb5a6156b8fa1b4547b611e85969438116fa2 a997f1ecd85e1467d11252741d188fac8db22722 6b3471f9ab0e75d618d5db2eea41e880725621ed f53bc5d847004e03b4dbc25a471e71f22d4f5c4b 2f04a3d8eacfa22a299e0c5a7edcc69d0fdd5933 44dce89a2e8abf4facf58f8457e9271c6a3f06c3 e71daa9581dc8114fb2ee13c70e9e65fa40df55a 0b189c41424aaa4f0cc24b1e2a5d2e0b79be3b60 1f40ac9340ee55cc371f179b6457ff64676bbc9d 313fe763bb5cd73c8ce8ecc6b143685b4a4c7639 299e8e59f445aca43f755f31916fea7580e34ac9 d6bfe9d7518504eb000ee688fca4ab611155df5d 982716e6410fdd33e73ce896a6013722a07af142 58deed579764de46b554956a15cf7f8bc64ac37e e4e769b2b51349c91e9f7f67218a042ed3080d44 adaebaa910a138901a248e0bc95b3da2711268a6 b2f99e0e8b560261bd12d9de91736a0467ab4574 6780949307c3dbf5e974ba21a3b0e88770f1a38d 2441d96fad24c3357d15dbe995fa746d3ff1a843 8c3477bcf0ca19c473177593b95860490ed8baf5
["Biology"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Engineering","Mathematics"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Biology"]
2019 2011 2012 2016 2019 2018 2019 2018 2019 2017 2018 2016 2018 2016 2018 2017 2019 2017 2018 2018 2018 2019 2020 2019 2018 2019 2019 2018 2020 2019 2020 2019 2019 2017 2017 2017 2016 2018 2017 2019 2016 2017 2019 2020 2020 2020 2019 2016 2017 2016 2019
UAI ICLR AIRT@MICCAI ICPR ICIP CVPR NIPS ESANN ICLR ECCV 2017 IEEE International Conference on Computer Vision (ICCV) International Journal of Computer Vision 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NeurIPS 2018 International Conference on 3D Vision (3DV) ECCV (9) ICMLA AAAI BigData 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) International Journal of Computer Vision NIPS ICLR ACL (1) WACV ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS
https://api.semanticscholar.org/250e5b30efe46032ddc290374ffa32de74d7fb30 https://api.semanticscholar.org/57156f73746d295f5ed73948c8a43dd024048392 https://api.semanticscholar.org/63cbac5a39cd926a806f60116b845f9bd70f5544 https://www.semanticscholar.org/paper/35c1668dc64d24a28c6041978e5fcca754eb2f4b https://api.semanticscholar.org/cd7bc01b27aa97d1d1504f77ec04fe9cecc4e21e https://api.semanticscholar.org/a2a7e4fa70b2a81ce66bc8d76e0b41e0d98b417e https://api.semanticscholar.org/86d0818ddcc9a670f25026ee1012d850289d87e1 https://api.semanticscholar.org/f87c9348e466681075044b629c71b738c205e12c https://api.semanticscholar.org/c5d2edd9f746c4e0eadfb33e06fc353eb2fa8887 https://api.semanticscholar.org/508eb5a6156b8fa1b4547b611e85969438116fa2 https://api.semanticscholar.org/a997f1ecd85e1467d11252741d188fac8db22722 https://www.semanticscholar.org/paper/35da0a2001eea88486a5de677ab97868c93d0824 https://www.semanticscholar.org/paper/148eb2b649ce0f66214fceba5c061b417ab88d79 https://www.semanticscholar.org/paper/8388f1be26329fa45e5807e968a641ce170ea078 https://www.semanticscholar.org/paper/3d5d9d8e74b215609eabba80ef79a35ebf460e49 https://www.semanticscholar.org/paper/28eceb438da0b841bbd3d02684dbfa263838ed60 https://www.semanticscholar.org/paper/4b71ab38d3ac6eee0da5e68ef666d33d1028bad8 https://www.semanticscholar.org/paper/9ec59d3b0320fc8abfaf297be45f0da6fdeef3d7 https://www.semanticscholar.org/paper/302207c149bdf7beb6e46e4d4afbd2fa9ac02c64 https://www.semanticscholar.org/paper/3d597e16ef56119bb6e6d78e3f379623862f0752 https://www.semanticscholar.org/paper/3a8d537bcec370d37990d39eab01c729496ad057 https://api.semanticscholar.org/6b3471f9ab0e75d618d5db2eea41e880725621ed https://api.semanticscholar.org/f53bc5d847004e03b4dbc25a471e71f22d4f5c4b https://api.semanticscholar.org/2f04a3d8eacfa22a299e0c5a7edcc69d0fdd5933 https://api.semanticscholar.org/44dce89a2e8abf4facf58f8457e9271c6a3f06c3 https://api.semanticscholar.org/e71daa9581dc8114fb2ee13c70e9e65fa40df55a https://api.semanticscholar.org/0b189c41424aaa4f0cc24b1e2a5d2e0b79be3b60 https://api.semanticscholar.org/1f40ac9340ee55cc371f179b6457ff64676bbc9d https://api.semanticscholar.org/313fe763bb5cd73c8ce8ecc6b143685b4a4c7639 https://api.semanticscholar.org/299e8e59f445aca43f755f31916fea7580e34ac9 https://api.semanticscholar.org/d6bfe9d7518504eb000ee688fca4ab611155df5d https://api.semanticscholar.org/982716e6410fdd33e73ce896a6013722a07af142 https://api.semanticscholar.org/58deed579764de46b554956a15cf7f8bc64ac37e https://www.semanticscholar.org/paper/345afa0e85cb2f5cb438ae44027499ff2c392409 https://www.semanticscholar.org/paper/0496691c6d54fd746c111d1004349acf7654145e https://www.semanticscholar.org/paper/9ec59d3b0320fc8abfaf297be45f0da6fdeef3d7 https://www.semanticscholar.org/paper/2b507f659b341ed0f23106446de8e4322f4a3f7e https://www.semanticscholar.org/paper/3d5d9d8e74b215609eabba80ef79a35ebf460e49 https://www.semanticscholar.org/paper/41d08fb733f3e50ac183490f84d6377dffccf350 https://www.semanticscholar.org/paper/4b71ab38d3ac6eee0da5e68ef666d33d1028bad8 https://www.semanticscholar.org/paper/35da0a2001eea88486a5de677ab97868c93d0824 https://www.semanticscholar.org/paper/d2e4587744a89bad95fea69e08842cad6c8ff0dd https://api.semanticscholar.org/e4e769b2b51349c91e9f7f67218a042ed3080d44 https://api.semanticscholar.org/adaebaa910a138901a248e0bc95b3da2711268a6 https://api.semanticscholar.org/b2f99e0e8b560261bd12d9de91736a0467ab4574 https://api.semanticscholar.org/6780949307c3dbf5e974ba21a3b0e88770f1a38d https://api.semanticscholar.org/2441d96fad24c3357d15dbe995fa746d3ff1a843 https://www.semanticscholar.org/paper/2b507f659b341ed0f23106446de8e4322f4a3f7e https://www.semanticscholar.org/paper/41d08fb733f3e50ac183490f84d6377dffccf350 https://www.semanticscholar.org/paper/bda89e0d181eda7e49ea831225eda86d075e111c https://api.semanticscholar.org/8c3477bcf0ca19c473177593b95860490ed8baf5