Authors: Inna Stainvas, Nathan Intrator, and Amiram Moshaiov (Tel Aviv University)
Abstract: Learning a many-parameter model is generally an under-constrained problem that requires additional regularization. We study reconstruction as well as several information theoretic constraints and show their relevance to recognition of corrupted inputs.
Results are demonstrated on a well known face recognition task in various resolutions and image degradations.