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
テキストから画像を生成するGANまとめ - akmtn記録
この記事は，テキストから画像を生成するGANについて横断的にまとめることを目指しました． "text-to-image"と呼ばれるタスクであり，テキスト（キャプション）を条件として，そのテキストにあう画像を生成することを目指します． 有名な研究では，StackGANがあります． 以下目次です． References なぜテキストから画像を生成するのか？ どういう生成モデルが優れているのか？ このタスクを困難にしている要因は？ 自然画像の高次元空間 テキスト空間と画像空間の違い データの用意 text-to-image synthesisの研究の流れは？ どういう画像を生成するか？ 使われているデータセットは？ 生成できる解像度は？ ネットワーク構造はどうなっているか？ Loss関数やDiscriminatorに工夫はあるか？ 雑感 おわりに References [1] GAN-INT-C
CycleGAN
Transferring characteristics from one image to another is an exciting proposition. How cool would it be if you could take a photo and convert it into the style of Van Gogh or Picasso! These are examples of cross domain image transfer - we want to take an image from an input domain $D_i$ and then transform it into an image of target domain $D_t$ without necessarily having a one-to-one mapping betwe
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
The GAN Zoo
Pretty painting is always better than a TerminatorEvery week, new papers on Generative Adversarial Networks (GAN) are coming out and it’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers are naming these GANs! You can read more about GANs in this Generative Models post by OpenAI or this overview tutorial in KDNuggets. Explosive growth — All the named
Generative models
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they might be going. One of our core aspirations at OpenAI is to develop algorithms and
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Generative Adversarial Networks (GAN) の学習方法進展・画像生成・教師なし画像変換