Papers › Learning Deep Parsimonious Representations
Learning Deep Parsimonious Representations
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | CUB-200 - 0-Shot Learning | Sample Clustering | Accuracy | 44.3% | #3 of 3 | Archive leaderboard | report |
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Methods
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