Flowreg: Latent Space Regularization Using Normalizing Flow For Limited Samples Learning

Abstract

Modern deep neural network models have made remarkable success in many areas, supported by large sets of training samples. Yet the hunger for huge data has also become fatal in further expanding the use of deep models. Limited sample learning aims at learning generalized and transferable representations, without requiring large training data. In this paper, we propose FlowReg, a new learnable latent space regularization for limited sample problems. FlowReg modulates the latent space using a Normalizing Flow with a simple prior (such as Gaussian) while maintaining the complexity of the posterior distribution. We conduct thorough experiments on diverse tasks in limited label learning, as well as detailed in-depth analysis to comprehensively demonstrate the effectiveness of FlowReg.