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[R] Computer Vision with a Single Robust Classifier

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.

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.

submitted by /u/andrew_ilyas
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.