Learning Deep Parsimonious Representations

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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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Few-Shot Image Classification CUB-200 - 0-Shot Learning Sample Clustering Accuracy 44.3% # 3