Abstract
In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://github.com/tmlr-group/MOKD.
Original language | English |
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Title of host publication | Proceedings of 41th International Conference on Machine Learning, ICML 2024 |
Editors | Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp |
Publisher | ML Research Press |
Pages | 48154-48185 |
Number of pages | 32 |
Publication status | Published - 21 Jul 2024 |
Event | 41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria Duration: 21 Jul 2024 → 27 Jul 2024 https://icml.cc/ https://openreview.net/group?id=ICML.cc/2024/Conference#tab-accept-oral https://proceedings.mlr.press/v235/ |
Publication series
Name | Proceedings of the International Conference on Machine Learning |
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Name | Proceedings of Machine Learning Research |
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Volume | 235 |
ISSN (Print) | 2640-3498 |
Conference
Conference | 41st International Conference on Machine Learning, ICML 2024 |
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Country/Territory | Austria |
City | Vienna |
Period | 21/07/24 → 27/07/24 |
Internet address |
Scopus Subject Areas
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Statistics and Probability