zhiz-m's Stars
zhiz-m/gba_rust
zhiz-m/chess-engine
Sha-N0/linux-setup
The better setup
zhiz-m/local-dev-setup
My personal setup
cp-algorithms/cp-algorithms
Algorithm and data structure articles for https://cp-algorithms.com (based on http://e-maxx.ru)
insou22/typing-the-technical-interview-rust
https://aphyr.com/posts/342-typing-the-technical-interview translated from Haskell to Rust
zhiz-m/octave_rust
Discord music bot written in Rust
junyanz/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
irfanICMLL/Auto_painter_demo
The code of building a web demo for Auto_painter
irfanICMLL/Auto_painter
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.