Blog Post: http://gradientscience.org/robust_apps/

Paper: http://gradientscience.org/robust-apps.pdf

TL;DR: Bunch of Computer Vision applications (generation, superresolution, inpainting, etc.) with just a single robustly trained classifier, straightforwardly scales to (1K-class, 224px) ImageNet.

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We show that a single classifier trained on a standard dataset can be leveraged for diverse computer vision applications. Using *only* an adversarially trained classifier (no generative architecture, just a standard ResNet trained with cross-entropy loss), we show that we can perform image generation, super resolution, inpainting, and interactive editing. The approach shows no instability and trivially scales to full (224×224) ImageNet. Our results suggest the robust classification framework as a viable alternative to more complex or task-specific approaches.