Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers

arXiv:2405.03730v1 Announce Type: new
Abstract: Deep neural networks are applied in more and more areas of everyday life. However, they still lack essential abilities, such as robustly dealing with spatially transformed input signals. Approaches to mitigate this severe robustness issue are limited to two pathways: Either models are implicitly regularised by increased sample variability (data augmentation) or explicitly constrained by hard-coded inductive biases. The limiting factor of the former is the size of the data space, which renders sufficient sample …

abstract arxiv classifiers cs.cv cs.lg head hidden however issue life networks neural networks robustness spatial type