MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

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 languageEnglish
Title of host publicationProceedings of 41th International Conference on Machine Learning, ICML 2024
EditorsRuslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp
PublisherML Research Press
Pages48154-48185
Number of pages32
Publication statusPublished - 21 Jul 2024
Event41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria
Duration: 21 Jul 202427 Jul 2024
https://icml.cc/
https://openreview.net/group?id=ICML.cc/2024/Conference#tab-accept-oral
https://proceedings.mlr.press/v235/

Publication series

NameProceedings of the International Conference on Machine Learning
NameProceedings of Machine Learning Research
Volume235
ISSN (Print)2640-3498

Conference

Conference41st International Conference on Machine Learning, ICML 2024
Country/TerritoryAustria
CityVienna
Period21/07/2427/07/24
Internet address

Scopus Subject Areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability

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