Unsupervised Deep Learning: Inter-Model Interpretation and Deep Clustering

Abstract

Deep Learning has risen recently as the de-facto machine learning approach for complex and rich-data domains. However, this has been established so far mostly in the setting of supervised learning. Supervised learning requires a tremendous amount of labeled data that is expensive, time-consuming, and not scalable. To overcome those limitations, researchers have invested recently in unsupervised deep learning approaches which make use of the huge amount and easily available unlabeled data. In this dissertation, our work in the domain of unsupervised deep learning was two-fold. We rst apply a novel model interpretation technique to study the learned concepts of di erent unsupervised deep learning approaches and how they compare to each other. Second, we considered one of the proposed approaches, Deep Cluster, proposing an explanation on why it works, supporting it with experiments, and following it by a series of ablation experiments to understand some parameters-performance dynamics.