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Context Encoders: Feature Learning by Inpainting

Context Encoders: Feature Learning by Inpainting

Deepak Pathak
Phillip Krähenbühl
Jeff Donahue
Trevor Darrell
Alexei A. Efros
UC Berkeley
Accepted at CVPR 2016
Context Encoders: Feature Learning by Inpainting
Semantic Inpainting results on held-out images by Context Encoder.

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.


Paper and Supplementary Material


Context Encoders: Feature Learning by Inpainting [Full paper 15MB]   [arXiv]

Citation

Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell and Alexei A. Efros. Context Encoders: Feature Learning by Inpainting. In CVPR 2016 .

@inproceedings{pathakCVPR16context,     Author = {Pathak, Deepak and     Kr/"ahenb/"uhl, Philipp and     Donahue, Jeff and     Darrell, Trevor and     Efros, Alexei},     Title = {Context Encoders:     Feature Learning by Inpainting},     Booktitle = CVPR,     Year = {2016} }


Source Code Demo


Inpainting Demo

[GitHub]

Features Caffemodel

[Prototxt] [Model 17MB]


Browse More Results

These are Context Encoder results on random crops of "held-out" images. Center half region is inpainted by our method. I do not show the cut-out context here for crispness. Look closely to find the boundary between context and the region inpainted by Context Encoders.

Click links below, hover over images and click to zoom-in. Don’t worry about mentioned sizes, images appear on a rolling basis so you can still see results while full file is being loaded.

StreetView Inpainting

[1500 Images, 35MB]

Imagenet Inpainting

[1500 Images, 40MB]

Context Encoders: Feature Learning by Inpainting
Qualitative illustration of the task w/ an example of inpainting by human artist.

Acknowledgements

The authors would like to thank Amanda Buster for the artwork in figure above, as well as Shubham Tulsiani and Saurabh Gupta for helpful discussions. This work was supported in part by DARPA, AFRL, Intel, DoD MURI award N000141110688, NSF awards IIS-1212798, IIS-1427425, and IIS-1536003, the Berkeley Vision and Learning Center and Berkeley Deep Drive. We also thank NVIDIA for gpu donation.

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