Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning

Authors

  • YongJin Yang KAIST AI
  • Taehyeon Kim KAIST AI
  • Se-Young Yun KAIST AI

DOI:

https://doi.org/10.1609/aaai.v38i15.29573

Keywords:

ML: Representation Learning, ML: Transfer, Domain Adaptation, Multi-Task Learning

Abstract

Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from various domains with only a few samples at hand. While prior approaches have primarily focused on parameter-efficient methods of using adapters, they often overlook two critical issues: shifts in batch statistics and noisy sample statistics arising from domain discrepancy variations. In this paper, we introduce Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation (ProLAD), marking two principal contributions. First, our methodology utilizes two separate adapters: one devoid of a normalization layer, which is more effective for similar domains, and another embedded with a normalization layer, designed to leverage the batch statistics of the target domain, thus proving effective for dissimilar domains. Second, to address the pitfalls of noisy statistics, we deploy two strategies: a progressive training of the two adapters and an adaptive distillation technique derived from features determined by the model solely with the adapter devoid of a normalization layer. Through this adaptive distillation, our approach functions as a modulator, controlling the primary adapter for adaptation, based on each domain. Evaluations on standard cross-domain few-shot learning benchmarks confirm that our technique outperforms existing state-of-the-art methodologies.

Published

2024-03-24

How to Cite

Yang, Y., Kim, T., & Yun, S.-Y. (2024). Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 16370-16378. https://doi.org/10.1609/aaai.v38i15.29573

Issue

Section

AAAI Technical Track on Machine Learning VI