Self-supervision for data interpretability in image classification and sample efficiency in reinforcement learning
Self-Supervised Learning (SSL), or learning representations of data by exploiting inherent structure present in it without labels, has driven significant progress in machine learning over the past decade, and in computer vision in particular over the past two years. In this work, we explore applications of SSL towards two separate goals - first, as a tool for efficiently interpreting datasets and model decisions, and second, as a tool for pretraining in reinforcement learning (RL) to greatly advance sample efficiency in that setting.  Chapter 1 introduces background material necessary to understand the remainder of this thesis. In particular, it provides an overview of Machine Learning, Deep Learning, Self-Supervised Representation Learning, and (Deep) Reinforcement Learning.  Chapter 2 briefly detours away from this thesis' focus on self-supervision, to examine how the phenomena of memorization manifests in deep neural networks. These results are then used to partially justify work presented in Chapter 3, which examines how self-supervision can be used to efficiently uncover structural regularity in training datasets, and to estimate training memorization and the influence of training samples on test samples. Recent experimental work on understanding the importance of memorizing the long-tail of data is also revisited.  Chapter 4 demonstrates how a combination of SSL pretraining objectives designed for the structure of data in RL can greatly improve sample efficiency to nearly human-level performance. Furthermore, it is shown that SSL enables the use of larger models, which has historically been a challenge in deep RL.  Chapter 5 concludes by reviewing the contributions of this work, and discusses future directions.
machine learning deep learning representation learning self-supervised learning reinforcement learning generalization