TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.

SOLUTION: A unified statistical view of the broad class of deep generative models 

PROBLEM: We propose a rejection sampling scheme using the discriminator of a GAN to
 approximately correct errors in the GAN generator distribution. We show that
 under quite strict assumptions, this will allow us to recover the data distribution
 exactly. We then examine where those strict assumptions break down and design a
 practical algorithm—called Discriminator Rejection Sampling (DRS)—that can be
 used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of
 Gaussians and on the state of the art SAGAN model. On ImageNet, we train an
 improved baseline that increases the best published Inception Score from 52.52 to
 62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use
 DRS to further improve on this baseline, improving the Inception Score to 76.08
 and the FID to 13.75.

SOLUTION: We use a GAN discriminator to perform an approximate rejection sampling scheme on the output of the GAN generator.

PROBLEM: Previous work (Bowman et al., 2015; Yang et al., 2017) has found difficulty developing generative models based on variational autoencoders (VAEs) for text. To address the problem of the decoder ignoring information from the encoder (posterior collapse), these previous models weaken the capacity of the decoder to force the model to use information from latent variables. However, this strategy is not ideal as it degrades the quality of generated text and increases hyper-parameters. In this paper, we propose a new VAE for text utilizing a multimodal prior distribution, a modified encoder, and multi-task learning. We show our model can generate well-conditioned sentences without weakening the capacity of the decoder. Also, the multimodal prior distribution improves the interpretability of acquired representations.

SOLUTION:

Show your work and conclude with the exact phrasing "Therefore, the final answer is [answer]." where [answer] is just the final number or expression that solves the problem.
user
We propose a model of variational autoencoders for text modeling without weakening the decoder, which improves the quality of text generation and interpretability of acquired representations.

