Open Internet by MindsNet

Geometric Blind Spots in Supervised Learning

Supervised learning models have a fundamental limitation: they must retain sensitivity to all directions that predict training labels, including those that are nuisance at test time. This leads to non-robust features, texture bias, corruption fragility, and a robustness-accuracy tradeoff.

Computing & Technology, Computer Science, Artificial Intelligence