Unsupervised Learning of slow features for Data Efficient Regression

Research in computational neuroscience suggests that the human brain's unparalleled data efficiency is a result of highly efficient mechanisms to extract and organize slowly changing high level features from continuous sensory inputs. In this paper, we apply this slowness principle to a state of the art representation learning method with the goal of performing data efficient learning of down-stream regression tasks. To this end, we propose the slow variational autoencoder (S-VAE), an extension to the $\beta$-VAE which applies a temporal similarity constraint to the latent representations. We empirically compare our method to the $\beta$-VAE and the Temporal Difference VAE (TD-VAE), a state-of-the-art method for next frame prediction in latent space with temporal abstraction. We evaluate the three methods against their data-efficiency on down-stream tasks using a synthetic 2D ball tracking dataset, a dataset from a reinforcent learning environment and a dataset generated using the DeepMind Lab environment. In all tasks, the proposed method outperformed the baselines both with dense and especially sparse labeled data. The S-VAE achieved similar or better performance compared to the baselines with $20\%$ to $93\%$ less data.