Computer Vision
arixv: ComboGAN: Unrestrained Scalability for Image Domain Translation accpeted to ICLR2018 workshop reddit github Scalibility issue for multi domains CycleGAN : Two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. Propose a multi-component
Computer Vision
StarGAN : accepted as CVPR2018 oral presentation. github arxiv (a) Each domain shift needs generators. (b) Share one generator and use latent code of each domain The previous limitation of pix2pix, DTN, CycleGAN & DiscoGAN, BicycleGAN is that they only handle two domains: the source and the target. But, there are
Computer Vision
Limitations of pix2pix, DTN, DiscoGAN & CycleGAN? * They produce single answer. * They are deterministic models. * Translates an image in one-to-one * Paired set, One-to-One : pix2pix (CVPR2017) * Unpaired set, One-to-One : DTN (ICLR2017), CycleGAN (ICCV2017) * Paired set, One-to-Many : ??? BicycleGAN: Toward Multimodal Image-to-Image Translation
ghost
In short; Assuming you have Ghost v1.0.0 (or above) with default theme, Casper. Default font is Georgia. We will change font with Google Web fonts. cd {your-ghost-dir}/content/themes/casper # move to theme directory npm install gulp-cli -g # install gulp globally npm install # install npm
ghost
1 귀찮아서 업그레이드를 미루고 있었는데, 거대 업데이트를 한참 전에 했길래 간만에 업그레이드했다. 간만에 블로그 만지작 거리는게 재밌었는데 이내 업그레이드는 커다란 귀찮음을 가져다주었다. 결론적으로 서버의 리눅스가 Ubuntu 16.04가 아니면 자잘한 에러가 많다. 이번 버전부터 ghost-cli를 통해 v1이상에서 쉽게 업그레이드가 가능토록 cli를 지원한다. 역시 갓갓 cli다. 그리고 힘들게 Nginx등을 설정해줬었는데(
Computer Vision
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (CycleGAN) from UC Berkeley (pix2pix upgrade) & Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (DiscoGAN) from SK T-Brain DiscoGAN & CycleGAN * Almost Identical concept. * DiscoGAN came 15 days earlier. Low resolution ($64 \times 64$) * CycleGAN has
Computer Vision
2. Unsupervised Cross-Domain Image Generation (DTN) published to ICLR2017 by Yaniv Taigman, Adam Polyak, Lior Wolf Learn $ G: S \rightarrow T $ of two related domains, $ S $ and $ T $ without labels! (labels of images are usually expensive) Baseline model $ D $ : discriminator, $ G $ : generator, $ f $ : context encoder. outputs feature. (128-dim)
Computer Vision
Image-to-Image Translation with Conditional Adversarial Networks (pix2pix) published to CVPR2017 by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros Learn pair-wise images of $S$ and $T$ like below * BW & Color image * Street Scene & Label * Facade & Label * Aerial & Map * Day & Night
Computer Vision
Problem statement of Image Translation Learn $G: (S \rightarrow T)$ $G$ that convert an image of source domain $S$ to an image of target domain $T$ Domain Adaptation/Transfer of an image. Paired and Unpaired dataset Image Translation: $S$ and $T$ are pair-wise labeled in dataset Image Translation: $S$
Linux
dotfiles and materials available at @junhocho[1] GUI보다 좋은 CUI, CUI. Command Line Interface를 쓰시면 어디서든 쉽게, 인터넷만 있고, Terminal이나 Putty가 있으면 개발을 할 수 있다. 이런 팀뷰어 없이도 말이다. CLI. 그래서 어떻게? ( Tmux + VIM ) 를 사용하자 CLI를 사용하는 이유. 1. Portable (인터넷이 구리면 팀뷰어는 힘들지) 2. Simple 3. Automated
Linux
본 포스트는 APUE chap.19 을 참고하여 작성함. Pseudo Terminal은 application 이 보기에는 터미널로 보이지만, 사실 진짜 터미널이 아닌 것이다. Pseudo Terminal의 구조, 예시, 사용법에 대해 설명하도록 함. Pseudo Terminal 개요 Pseudo terminal 을 사용하는 프로세스들의 전형적인 구조는 아래와 같다. * 부모 프로세스가 pseudo terminal master를 open 하고 fork -> 자식
기본적인 과정 * Itemize (item 분리) * Shape (item 별 glyph 추출) * Place (?) * Render 1. Itemize a. Uniscribe HRESULT ScriptItemize( _In_ const WCHAR *pwcInChars, _In_ int cInChars, _In_ int cMaxItems, _In_opt_ const SCRIPT_CONTROL *psControl, _In_opt_ const SCRIPT_STATE *psState, _Out_ SCRIPT_ITEM *pItems, _Out_ int *pcItems ); pItems 이 떨어지는