quantshah

" Use vim-plug
call plug#begin('~/.local/share/nvim/plugged')
Plug 'Shougo/deoplete.nvim', { 'do': ':UpdateRemotePlugins' }
Plug 'zchee/deoplete-jedi'
Plug 'Shougo/echodoc.vim'
Plug 'junegunn/fzf', { 'dir': '~/.fzf', 'do': './install --all' }
Plug 'junegunn/fzf.vim'
Plug 'scrooloose/nerdcommenter'
Plug 'vim-airline/vim-airline'
Plug 'vim-airline/vim-airline-themes'

brannondorsey

Image-to-Image Translation with Conditional Adversarial Networks

Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016

• Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
• GANs learn a loss function rather than using an existing one.
• GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
• Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
• The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
• The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
• Unlike an unconditional GAN, both th