Using learned optimizers to make models robust to input noise

8 Jun 2019Luke MetzNiru MaheswaranathanJonathon ShlensJascha Sohl-DicksteinEkin D. Cubuk

State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models are tested on corrupted images (e.g. due to scale changes, translations, or shifts in brightness or contrast), performance degrades significantly... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.