Adversarial Adaptive Interpolation for Regularizing Representation Learning and Image Synthesis in Autoencoders

Data interpolation is typically used to explore and understand the latent representation learnt by a deep network. Naive linear interpolation may induce mismatch between the interpolated data and the underlying manifold of the original data. In this paper, we propose an Adversarial Adaptive Interpolation (AdvAI) approach for facilitating representation learning and image synthesis in autoencoders. To determine an interpolation path that stays on the manifold, we incorporate an interpolation correction module, which learns to offset the deviation from the manifold. Further, we perform matching with a prior distribution to control the characteristics of the representation. The data synthesized from random codes along with interpolation-based regularization are in turn used to constrain the representation learning process. In the experiments, the superior performance of the proposed approach demonstrates the effectiveness of AdvAI and associated regularizers in a variety of downstream tasks.