DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models

Understand

Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of the real world, even to naturally occurring ones.

• A vast majority of current approaches have focused on data-augmentation methods to expand the range of perturbations that the classifier is exposed to while training.
• A relatively unexplored avenue that is equally promising involves sanitizing an image as a preprocessing step, depending on the nature of perturbation.
• In this paper, we propose to use control for learned models to recover from distribution shifts online.