Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Generic generation and manipulation of text is challenging and has limited success
compared to recent deep generative modeling in visual domain. This paper aims at …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Recent work on generative text modeling has found that variational autoencoders (VAE) with
LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015) …

Binary classifiers are employed as discriminators in GAN-based unsupervised style transfer
models to ensure that transferred sentences are similar to sentences in the target domain …

Deep generative models have achieved remarkable success in various data domains,
including images, time series, and natural languages. There remain, however, substantial …

Deep latent variable models, trained using variational autoencoders or generative
adversarial networks, are now a key technique for representation learning of continuous …

Virtual try-on systems under arbitrary human poses have significant application potential, yet
also raise extensive challenges, such as self-occlusions, heavy misalignment among …