luethan2025/context-encoder

Quick Start

git clone https://github.com/bareform/context-encoder.git
cd context-encoder

You can download the pre-trained models here and use the provided Jupyter Notebook inference.ipynb to generate some samples.

Method

Feature Learning by Inpainting

Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros

University of California, Berkeley

We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.

Citation

The original paper can be found at:

@misc{pathak2016context,
    title={Context Encoders: Feature Learning by Inpainting},
    author={Deepak Pathak and Philipp Krahenbuhl and Jeff Donahue and Trevor Darrell and Alexei A. Efros},
    year={2016},
    eprint={1604.07379},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}