Deep Autoencoders: From Understanding to Generalization Guarantees
This work addresses the generalization mystery in deep learning for researchers and practitioners using autoencoders, offering incremental improvements through new regularization techniques.
The paper tackled the problem of understanding and improving generalization in deep autoencoders by interpreting their piecewise affine structure and deriving new regularizations that capture data symmetry, resulting in methods that outperform state-of-the-art regularization techniques in experiments.
A big mystery in deep learning continues to be the ability of methods to generalize when the number of model parameters is larger than the number of training examples. In this work, we take a step towards a better understanding of the underlying phenomena of Deep Autoencoders (AEs), a mainstream deep learning solution for learning compressed, interpretable, and structured data representations. In particular, we interpret how AEs approximate the data manifold by exploiting their continuous piecewise affine structure. Our reformulation of AEs provides new insights into their mapping, reconstruction guarantees, as well as an interpretation of commonly used regularization techniques. We leverage these findings to derive two new regularizations that enable AEs to capture the inherent symmetry in the data. Our regularizations leverage recent advances in the group of transformation learning to enable AEs to better approximate the data manifold without explicitly defining the group underlying the manifold. Under the assumption that the symmetry of the data can be explained by a Lie group, we prove that the regularizations ensure the generalization of the corresponding AEs. A range of experimental evaluations demonstrate that our methods outperform other state-of-the-art regularization techniques.