DOMAIN-AGNOSTIC META-LEARNING FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION

Few-shot classification requires one to classify instances of novel classes, given only a few examples of each class. Although promising meta-learning methods have been proposed recently, there is no guarantee that existing solutions would generalize to novel classes from an unseen domain. In this paper, we tackle the challenging task of cross-domain few-shot classification and propose Domain-Agnostic Meta-Learning (DAML) algorithm. Our DAML, serving as an optimization strategy, learns to adapt the model to novel classes in both seen and unseen domains by data sampled from multiple domains with desirable task settings. In our experiments, we apply DAML on three popular metric-based models under cross-domain settings. Experiments on several benchmarks (mini-ImageNet, CUB, Cars, Places, Plantae and META-DATASET) show that DAML significantly improves the generalization ability of learning models, and addresses cross-domain few-shot classification with promising results.