On invariance and selectivity in representation learning

TitleOn invariance and selectivity in representation learning
Publication TypeJournal Article
Year of Publication2016
AuthorsPoggio T., Rosasco L., Anselmi F.
JournalInformation and Inference: A Journal of the IMA
Date Published05/2016

We study the problem of learning from data representations that are invariant to transformations, and at the same time selective, in the sense that two points have the same representation if one is the transformation of the other. The mathematical results here sharpen some of the key claims of i-theory—a recent theory of feedforward processing in sensory cortex (Anselmi et al., 2013, Theor. Comput. Sci. and arXiv:1311.4158; Anselmi et al., 2013, Magic materials: a theory of deep hierarchical architectures for learning sensory representations. CBCL Paper; Anselmi & Poggio, 2010, Representation learning in sensory cortex: a theory. CBMM Memo No. 26).