Predictive variational autoencoder for learning robust representations of time-series data

TL;DR

This paper introduces a predictive variational autoencoder architecture that learns robust, smooth latent representations of time-series data by predicting future points and using a new model selection metric, improving interpretability.

Contribution

The authors propose a novel VAE model with a predictive task and a smoothness-based model selection metric to enhance the robustness of latent representations in time-series data.

Findings

The predictive VAE mitigates learning of spurious features.

The smoothness metric improves latent space quality.

The method accurately recovers latent factors on synthetic data.

Abstract

Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal behavior. However, without careful model selection, the uncovered latent factors may reflect noise in the data rather than true underlying features, rendering such representations unsuitable for scientific interpretation. Existing solutions to this problem involve introducing additional measured variables or data augmentations specific to a particular data type. We propose a VAE architecture that predicts the next point in time and show that it mitigates the learning of spurious features. In addition, we introduce a model selection metric based on smoothness over time in the latent space. We show that together these two constraints on VAEs to be smooth over time produce robust latent representations and faithfully recover latent factors on synthetic datasets.