Neural Information Processing Systems http://nips.cc/

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.

This is a very solid paper all around. The idea to cluster activations and force the network to "stay close" to a restricted set of representations is intuitive, acting as a parsimony constraint that additionally enables interpretability. While solid theoretical motivation and analysis isn't provided, the proposed algorithm feels natural and the experiments are fairly comprehensive and compelling. A broad range of tasks are considered, including unsupervised, fine-grained, and zero-shot learning in addition to standard classification, and the visualizations of cluster structure demonstrate that the cluster centers are meaningful---they convince me that the approach is performing as one would hope. Some specific points: It would be nice to see a table or chart demonstrating the effect of different choices of cluster size on generalization error (it is mentioned that cross-validation was used, but it would still be good to get a feel for the sensitivity with respect to choice of cluster size).

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. Papers published at the Neural Information Processing Systems Conference.

In this paper we aim at facilitating generalization for deep networks while supporting interpretabilityof 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, such as sample clustering, 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.