Few-Shot Learning as Domain Adaptation: Algorithm and Analysis

Understand

To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes.

• A major challenge for FSL is that the distribution of the unseen classes is different from that of those seen, resulting in poor generalization even when a model is meta-trained on the seen classes.
• This class-difference-caused distribution shift can be considered as a special case of domain shift.
• In this paper, for the first time, we propose a domain adaptation prototypical network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework.