GAN（と強化学習との関係）
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in re
GAN — Wasserstein GAN & WGAN-GP
Training GAN is hard. Models may never converge and mode collapses are common. To move forward, we can make incremental improvements or embrace a new path for a new cost function. Do cost functions matter in the GAN training? This article is part of the GAN series which looks into the Wasserstein GAN (WGAN) and the WGAN-Gradient penalty in details. The equations for Wasserstein GAN look very unapp
Modeling documents with Generative Adversarial Networks
I presented some preliminary work on using Generative Adversarial Networks to learn distributed representations of documents at the recent NIPS workshop on Adversarial Training. In this post I provide a brief overview of the paper and walk through some of the code. Learning document representations Representation learning has been a hot topic in recent years, in part driven by the desire to apply
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Generative Adversarial Networks (GAN) の学習方法進展・画像生成・教師なし画像変換
