Abstract
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a few labeled target-domain data and a well-trained source-domain classifier (i.e., a source hypothesis), for the additional information of the highly-compatible unlabeled data. However, the generated data of the existing methods are extremely similar or even the same. The strong dependency among the generated data will lead the learning to fail. In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC). Specifically, DEG-Net will generate data via minimizing the HSIC value (i.e., maximizing the independence) among the semantic features of the generated data. By DEG-Net, the generated unlabeled data are more diverse and more effective for addressing the FHA problem. Experimental results show that the DEG-Net outperforms existing FHA baselines and further verifies that generating diverse data plays a vital role in addressing the FHA problem.
Original language | English |
---|---|
Title of host publication | Proceedings of 40th International Conference on Machine Learning, ICML 2023 |
Editors | Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett |
Publisher | ML Research Press |
Pages | 8260-8275 |
Number of pages | 16 |
Publication status | Published - Jul 2023 |
Event | 40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States Duration: 23 Jul 2023 → 29 Jul 2023 https://icml.cc/Conferences/2023 https://proceedings.mlr.press/v202/ https://openreview.net/group?id=ICML.cc/2023/Conference |
Publication series
Name | Proceedings of Machine Learning Research |
---|---|
Publisher | ML Research Press |
Volume | 202 |
ISSN (Print) | 2640-3498 |
Conference
Conference | 40th International Conference on Machine Learning, ICML 2023 |
---|---|
Country/Territory | United States |
City | Honolulu |
Period | 23/07/23 → 29/07/23 |
Internet address |
Scopus Subject Areas
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Statistics and Probability