Learning Deep Parsimonious Representations

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun


In this paper we aim at facilitating generalization for deep networks while supporting interpretability of the learned representations. Towards this goal, we propose a clustering based regularization that encourages parsimonious representations. Our k-means style objective is easy to optimize and flexible supporting various forms of clustering, including sample and spatial clustering as well as co-clustering. We demonstrate the effectiveness of our approach on the tasks of unsupervised learning, classification, fine grained categorization and zero-shot learning.