Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Future Directions <s> As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines. <s> BIB001 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Future Directions <s> Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues. <s> BIB002 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Future Directions <s> Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks (RNNs) in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from ‘serialised’ MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data, and demonstrate results from differentially private training of the RCGAN. <s> BIB003 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Future Directions <s> Multivariate time series are characterized by a variety of missing values and the advanced analysis suffers. Conventional methods for imputing missing values, such as mean and zero imputation, deletion and matrix factorization, are not capable to model the temporal dependencies and the complex distribution of the multivariate time series. In this paper, we regard missing value imputation as a data generation problem. Inspired by the success of Generative Adversarial Networks (GAN) in image generation, we adopt GAN to learn the overall distribution of a multivariate time series dataset and to generate the missing values for each sample. Different from image completion where GAN is trained on complete images, we cannot obtain complete time series due to the nature of data recording process. Therefore, a modified Gate Recurrent Unit (GRU) model is employed in GAN to model the temporal irregularity of the incomplete time series. Experiments on two multivariate time series datasets show that our method outperforms the baselines in terms of accuracy of missing value imputation. Additionally, a simple model on the imputed data achieves state-of-the-art results on the two prediction tasks, which demonstrates the benefit of our imputation method for downstream applications. <s> BIB004 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Future Directions <s> Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model. <s> BIB005
GANs were originally proposed to produce plausible synthetic images and have achieved exciting performance in the computer vision area. GANs have been applied to some other fields, (e.g., time series generation  , BIB003 , BIB004 and natural language processing BIB005 , BIB001 - BIB002 ) with some success. Compared to computer vision, GANs research in other areas is still somewhat limited. The limitation is caused by the different properties inherent in image versus non-image data. For instance, GANs work to produce continuous value data but natural language are based on discrete values like words, characters, bytes, etc., so it is hard to apply GANs for natural language applications. Future research of course is being carried out for applying GANs to other areas.