Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training

Abstract

CNNs can be trained to acquire online-invariance: the same internal representation is elicited following different transformations of the same, novel objects• We demonstrate on-line invariance for the following transformations: translation, rotation, scale, brightness, contrast, and viewpoint• Many different supervised networks acquire this property, as does a self-supervised network that solves the same/different task• As few as 50 images taken 10 object classes are needed to train for online invariance