Generalized Adaptation for Few-Shot Learning

Understand

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model.

• In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model.
• Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model.
• We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases.