TY - JOUR
T1 - Learning deep parsimonious representations
AU - Liao, Renjie
AU - Schwing, Alexander
AU - Zemel, Richard S.
AU - Urtasun, Raquel
N1 - Funding Information:
This work was partially supported by ONR-N00014-14-1-0232, NVIDIA and the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC00003. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government.
Publisher Copyright:
© 2016 NIPS Foundation - All Rights Reserved.
PY - 2016
Y1 - 2016
N2 - 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, 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.
AB - 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, 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.
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M3 - Conference article
AN - SCOPUS:85018904825
SP - 5092
EP - 5100
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
T2 - 30th Annual Conference on Neural Information Processing Systems, NIPS 2016
Y2 - 5 December 2016 through 10 December 2016
ER -