Domain Generalized Few-Shot Image Classification via Meta Regularization Network

• Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3748 - 3752
• Main Authors: Zhang, Min, Huang, Siteng, Wang, Donglin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2022
• Subjects: Acoustics; Conferences; Domain Generalization; Feature extraction; Few-shot Learning; Image classification; Meta Learning; Meta Regularization Network; Signal processing; Speech processing
• DOI: 10.1109/ICASSP43922.2022.9747620

In few-shot image classification scenarios, meta-learning methods aim to learn transferable feature representations extracted from seen domains (base classes) in the meta-training phase and quickly adapt to unseen domains (novel classes) in the meta-testing phase. However, when seen and unseen domains have a large discrepancy, existing approaches do not perform well due to the incapability of generalizing to unseen domains. In this paper, we investigate the challenging domain generalized few-shot image classification problem. We design an Meta Regularization Network (MRN) to learn a domain-invariant discriminative feature space, where a learning to learn update strategy is used to simulate domain shifts caused by seen and unseen domains. The simulation trains the model to learn to reorganize the feature knowledge acquired from seen domains to represent unseen domains. Extensive experiments and analysis show that our proposed MRN can significantly improve the generalization ability of various meta-learning methods to achieve state-of-the-art performance in domain generalized few-shot learning.