context-encoder
Context Encoders is an open-source implementation of the CVPR 2016 paper Unsupervised Feature Learning by Image Inpainting using GANs. This software trains deep neural networks to perform image inpainting, the task of filling in missing pixels within an image, to learn robust unsupervised visual features. The framework jointly optimizes an encoder-decoder architecture using both reconstruction loss and adversarial loss from a Generative Adversarial Network. It supports two primary training modes: center region inpainting for standard conditioning and arbitrary random region inpainting for more flexible scenarios. The distribution includes training scripts, testing utilities, and pre-trained models for inference on various datasets such as Paris Street View, ImageNet, and UC Berkeley cityscapes. Built initially on the Torch deep learning framework based on Soumith Chintala's DCGAN implementation, the codebase allows researchers to reproduce state-of-the-art semantic inpainting results, experiment with differen