Features transfer learning for image and video recognition tasks

Abstract

Feature transfer learning aims to reuse knowledge previously acquired in some source dataset to apply it in another target data and/or task. A requirement for the transfer of knowledge is the quality of feature spaces obtained, in which deep learning methods are widely applied since those provide discriminative and general descriptors. In this context, the main questions include: what to transfer; how to transfer; and when to transfer. Hence, we address these questions through distinct learning paradigms, transfer learning techniques, and several datasets and tasks. Therefore, our contributions are: an analysis of multiple descriptors contained in supervised deep networks; a new generalization metric that can be applied to any model and evaluation system; and a new architecture with a loss function for semi-supervised deep networks, in which all available data provide the learning.