The sample complexity of a learning task is increased by transformations that do not change class identity. Visual object recognition for example, i.e. the discrimination or categorization of distinct semantic classes, is affected by changes in viewpoint, scale, illumination or planar transformations. We introduce a weakly-supervised framework for learning robust and selective representations from sets of transforming examples (orbit sets). We train deep encoders that explicitly account for the equivalence up to transformations of orbit sets and show that the resulting encodings contract the intra-orbit distance and preserve identity either by preserving reconstruction or by increasing the inter-orbit distance. We explore a loss function that combines a discriminative term, and a reconstruction term that uses a decoder-encoder map to learn to rectify transformation-perturbed examples, and demonstrate the validity of the resulting embeddings for one-shot learning. Our results suggest that a suitable definition of orbit sets is a form of weak supervision that can be exploited to learn semantically relevant embeddings.

%B AAAI Spring Symposium Series, Science of Intelligence %C AAAI %G eng %U https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357