Open AccessProceedings Article
Learning Deep Parsimonious Representations
Renjie Liao,Alexander G. Schwing,Richard S. Zemel,Richard S. Zemel,Raquel Urtasun +4 more
- 01 Jan 2016
Vol. 29, pp 5076-5084
TL;DR: A clustering based regularization that encourages parsimonious representations is proposed that is easy to optimize and flexible supporting various forms of clustering, including sample and spatial clustering as well as co-clustering.
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Abstract: 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.
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