Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an im
Generative Adversarial Networks (GAN) ã®å¦ç¿’方法進展・画åƒç”Ÿæˆãƒ»æ•™å¸«ãªã—ç”»åƒå¤‰æ›
Generative Adversarial Networks (GAN) ã®å¦ç¿’方法進展・画åƒç”Ÿæˆãƒ»æ•™å¸«ãªã—ç”»åƒå¤‰æ›ã«é–¢ã—ã€Seminar ã§ã®é€²å±•ç´¹ä»‹ã«ç”¨ã„ãŸè³‡æ–™ã§ã™ã€‚Read less
生æˆãƒ¢ãƒ‡ãƒ«ã® Deep Learning
PFIセミナー(2016/02/25)ã§ç™ºè¡¨ã—ãŸã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™ã€‚ä¼æ‰¿ã‚µãƒ³ãƒ—リングå¯èƒ½ãªç”Ÿæˆãƒ¢ãƒ‡ãƒ«ã«é–¢ã™ã‚‹Deep Learningã®ç´¹ä»‹ã§ã™ï¼ˆã‚ーワード：Wake-Sleep, 変分 AutoEncoder, Generative Adversarial Nets, Likelihood Ratio）Read less
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
Chris Olah on Twitter: "Comparison of different GAN variants. I couldn't fit the full version in our paper. https://t.co/G0gVDincX9"
Comparison of different GAN variants. I couldn't fit the full version in our paper. https://t.co/G0gVDincX9
GitHub - reedscot/icml2016: Generative Adversarial Text-to-Image Synthesis
###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee This is the code for our ICML 2016 paper on text-to-image synthesis using conditional GANs. You can use it to train and sample from text-to-image models. The code is adapted from the excellent dcgan.torch. ####Setup Instructions You will need to install Torch, C
Improved Techniques for Training GANs [arXiv:1606.03498]
æ¦‚è¦ Improved Techniques for Training GANsã‚’èªã‚“ã Chainer 1.18ã§å®Ÿè£…ã—㟠アニメ顔画åƒã‚’å¦ç¿’ã•ã›ãŸ MNISTã®åŠæ•™å¸«ã‚ã‚Šå¦ç¿’を実験ã—㟠ã¯ã˜ã‚ã« ã“ã®è«–æ–‡ã¯GANã«ã‚ˆã‚‹ç”»åƒç”Ÿæˆã¨åŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã®2ã¤ã«ç„¦ç‚¹ã‚’当ã¦ã€æ–°ãŸãªå¦ç¿’テクニックをæ案ã—ãŸã‚‚ã®ã§ã™ã€‚ ã“ã®è¨˜äº‹ã§ã¯ãã®ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã®ä¸ã‹ã‚‰feature matchingã¨minibatch discriminationを実装ã—ã¾ã™ã€‚ ã•ã‚‰ã«å¤šã‚¯ãƒ©ã‚¹åˆ†é¡žå™¨ã‚’Discriminatorã¨ã—ã¦ä½¿ã†ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã‚’用ã„ã¦MNISTã®åŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã‚’è¡Œãªã„ã¾ã™ã€‚ 以下ã€è¨“練データを$x$ã€GeneratorãŒç”Ÿæˆã—ãŸãƒ‡ãƒ¼ã‚¿ã‚’$\bar{x}$ã¨ã—ã¾ã™ã€‚ ã¾ãŸDiscriminatorã‚’$D(x)$ã€ãƒŽã‚¤ã‚º$z$ã‹ã‚‰$\bar{x}$を生æˆã™ã‚‹Generatorã‚’$G(z)$ã¨è¡¨è¨˜ã—ã¾ã™ã€‚ GANã§ã¯$
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
VAEã¨GANを活用ã—ãŸãƒ•ã‚¡ãƒƒã‚·ãƒ§ãƒ³ã‚¢ã‚¤ãƒ†ãƒ 検索システム- ZOZO TECH BLOG
データサイエンティストã®ä¸æ‘ã§ã™ã€‚今回ã¯ã‚¤ãƒ¡ãƒ¼ã‚¸ãƒ•ã‚¡ãƒ¼ã‚¹ãƒˆãªãƒ•ã‚¡ãƒƒã‚·ãƒ§ãƒ³ã‚¢ã‚¤ãƒ†ãƒ 検索システムを作ã£ã¦ã¿ãŸã®ã§ãã¡ã‚‰ã®ç´¹ä»‹ã‚’ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ 本記事ã§ç´¹ä»‹ã™ã‚‹æŠ€è¡“ã¯IBIS2016ã§ã‚‚å ±å‘Šã—ã¦ã„ã¾ã™ã€‚ æ¦‚è¦ ãƒ•ã‚¡ãƒƒã‚·ãƒ§ãƒ³ã‚¢ã‚¤ãƒ†ãƒ ã‚’æŽ¢ã™ã¨ãã€è¦‹ãŸç›®ã®å°è±¡ã¯ã¨ã¦ã‚‚大事ãªè¦ç´ ã§ã™ã€‚ファッションã¯æ„Ÿè¦šçš„ãªã‚‚ã®ãªã®ã§ã€è‡ªåˆ†ãŒæ¬²ã—ã„æœã«ã¤ã„ã¦è¨€è‘‰ã§èª¬æ˜Žã™ã‚‹ã“ã¨ã¯é›£ã—ã„ã§ã™ãŒã€ãã®ã‚¢ã‚¤ãƒ†ãƒ ã®è‰¯ã—悪ã—ã¯ç”»åƒã‚’見ãŸã ã‘ã§åˆ¤æ–ã§ãã‚‹ã‹ã‚‰ã§ã™ã€‚ 今回開発ã—ãŸæ¤œç´¢ã‚·ã‚¹ãƒ†ãƒ ã¯è¦‹ãŸç›®ã®å°è±¡ã‚’大事ã«ã—ãŸã„ã®ã§ã€ç”»åƒã‚’クエリã¨ã—ã¾ã™ã€‚ãŸã ã—ã€ãŸã ã®ç”»åƒæ¤œç´¢ã§ã¯é¢ç™½ããªã„ã®ã§ã€è‰²ã‚„形状ãªã©ã®å±žæ€§æƒ…å ±ã‚’ä»˜åŠ ã—ãŸçŠ¶æ…‹ã§æ¤œç´¢ã‚’実行ã§ãるよã†ã«ã—ã¾ã—ãŸã€‚ 例ãˆã°ã€ã€Œã‚·ãƒ«ã‚¨ãƒƒãƒˆã¯è‰¯ã„ã‚“ã ã‘ã©ã€ã“ã‚Œã®èµ¤ã„ã‚„ã¤ãŒæ¬²ã—ã„ã€ã®ã‚ˆã†ãªæ„Ÿè¦šçš„ãªæ³¨æ–‡ã‚’ã€ä»¥ä¸‹ã®GIFã®ã‚ˆã†ã«ç”»åƒã«å±žæ€§ã‚’ä»˜åŠ ã™ã‚‹å½¢ã§æ‹¾ã£ã¦ã„ã¾ã™ã€‚ よãã‚る検索システムã§ã¯ã‚«ãƒ†ã‚´ãƒªã«
Create with AI
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Chainerã§é¡”イラストã®è‡ªå‹•ç”Ÿæˆ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? PFNã®mattyaã§ã™ã€‚chainerを使ã£ãŸã‚¤ãƒ©ã‚¹ãƒˆè‡ªå‹•ç”Ÿæˆã‚’ã‚„ã£ã¦ã¿ã¾ã—ãŸ(上ã®ç”»åƒã‚‚ãã®ä¸€ä¾‹ã§ã™)。 20日目ã®@rezoolabã•ã‚“ã®è¨˜äº‹(Chainerを使ã£ã¦ã‚³ãƒ³ãƒ”ュータã«ã‚¤ãƒ©ã‚¹ãƒˆã‚’æã‹ã›ã‚‹)ã¨ãƒã‚¿ãŒè¢«ã£ã¡ã‚ƒã£ãŸã®ã§ã€æœ¬è¨˜äº‹ã§ã¯ã•ã‚‰ã«ç™ºå±•çš„ãªã¨ã“ã‚を書ã„ã¦ã„ããŸã„ã¨æ€ã„ã¾ã™ã€‚一緒ã«èªã‚“ã§ã„ãŸã ãã¨ã‚ˆã„ã‹ã¨ã€‚ æ¦‚è¦ Chainerã§ç”»åƒã‚’生æˆã™ã‚‹ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã§ã‚ã‚‹DCGANを実装ã—ãŸâ†’github safebooruã‹ã‚‰é¡”イラストを集ã‚ã¦ãã¦å¦ç¿’ã•ã›ãŸ å¦ç¿’済ã¿ãƒ¢ãƒ‡ãƒ«ã‚’convnetjsã§èªã¿è¾¼ã¾ã›ã¦ã€ãƒ–ラウザ上ã§å‹•ãデ
zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks
Generated samples. Related code can be found here Motivation zi2zi is the follow-up work for my last project, once again tackling the same problem of style transfer between Chinese fonts. It is a pleasant surprise that the Rewrite project gets a fair amount of attention and interests, however, looking back, the result feels underwhelming. As an experimental attempt it does fulfill its purpose, but
ã¯ã˜ã‚ã¦ã®GAN
今回ã¯GAN（Generative Adversarial Network）を解説ã—ã¦ã„ãã¾ã™ã€‚ GANã¯â€œDeep Learningâ€ã¨ã„ã†æœ¬ã®è‘—者ã§ã‚‚ã‚ã‚‹Ian GoodfellowãŒè€ƒæ¡ˆã—ãŸãƒ¢ãƒ‡ãƒ«ã§ã™ã€‚NIPS 2016ã§ã‚‚GANã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ãŒè¡Œã‚れるãªã©éžå¸¸ã«æ³¨ç›®ã‚’集ã‚ã¦ã„る分野ã§ã€æ¬¡ã€…ã«è«–æ–‡ãŒå‡ºã¦ãã¦ã„ã¾ã™ã€‚ ã¾ãŸã€Quoraã®ã‚»ãƒƒã‚·ãƒ§ãƒ³ã§Yann LeCunãŒã€ã“ã®10å¹´ã®æ©Ÿæ¢°å¦ç¿’ã§æœ€ã‚‚é¢ç™½ã„アイディアã¨è¿°ã¹ã¦ã„ãŸã‚Šã‚‚ã—ã¾ã™ã€‚ “The most interesting idea in the last 10 years in ML, in my opinion.†–Yann LeCun GANã¯èžã„ãŸã“ã¨ã¯ã‚ã‚‹ã‘ã‚Œã©ã‚ã¾ã‚Šè¿½ãˆã¦ãªã„ã¨ã„ã†äººå‘ã‘ã«åŸºç¤Žã‹ã‚‰è§£èª¬ã—ã¦ã„ããŸã„ã¨æ€ã„ã¾ã™ã€‚ãã‚Œã§ã¯é †ã«è¦‹ã¦ã„ãã¾ã—ょã†ã€‚ 目次 基礎ç†è«– DCGAN 実装 論文紹介 ã¾ã¨ã‚ 基礎ç†
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GitHub - junyanz/iGAN: Interactive Image Generation via Generative Adversarial Networks
GitHub - Guim3/IcGAN: Invertible conditional GANs for image editing
GitHub - fxia22/PixelDTGAN: A torch implementation of "Pixel-Level Domain Transfer"
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