REPRESENTATION LEARNING FOR SEQUENCE DATA WITH DEEP AUTOENCODING PREDICTIVE COMPO-NENTS

Abstract

We propose Deep Autoencoding Predictive Components (DAPC) -a selfsupervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predictive information of latent feature sequences, which is the mutual information between the past and future windows at each time step. In contrast to the mutual information lower bound commonly used by contrastive learning, the estimate of predictive information we adopt is exact under a Gaussian assumption. Additionally, it can be computed without negative sampling. To reduce the degeneracy of the latent space extracted by powerful encoders and keep useful information from the inputs, we regularize predictive information learning with a challenging masked reconstruction loss. We demonstrate that our method recovers the latent space of noisy dynamical systems, extracts predictive features for forecasting tasks, and improves automatic speech recognition when used to pretrain the encoder on large amounts of unlabeled data. 1 

1. INTRODUCTION

Self-supervised representation learning methods aim at learning useful and general representations from large amounts of unlabeled data, which can reduce sample complexity for downstream supervised learning. These methods have been widely applied to various domains such as computer vision (Oord et al., 2018; Hjelm et al., 2018; Chen et al., 2020; Grill et al., 2020) , natural language processing (Peters et al., 2018; Devlin et al., 2019; Brown et al., 2020) , and speech processing (Schneider et al., 2019; Pascual et al., 2019b; Chung & Glass, 2020; Wang et al., 2020; Baevski et al., 2020) . In the case of sequence data, representation learning may force the model to recover the underlying dynamics from the raw data, so that the learnt representations remove irrelevant variability in the inputs, embed rich context information and become predictive of future states. The effectiveness of the representations depends on the self-supervised task which injects inductive bias into learning. The design of self-supervision has become an active research area. One notable approach for self-supervised learning is based on maximizing mutual information between the learnt representations and inputs. The most commonly used estimate of mutual information is based on contrastive learning. A prominant example of this approach is CPC (Oord et al., 2018) , where the representation of each time step is trained to distinguish between positive samples which are inputs from the near future, and negative samples which are inputs from distant future or other sequences. The performance of contrastive learning heavily relies on the nontrivial selection

funding

* Work done during an internship at Salesforce Research. † Work done while Weiran Wang was with Salesforce Research.

