In this paper, we try to understand FSL by exploring two key questions: (1) How to quantify the relationship between training and novel tasks?

The excess risk bound achieves the so-called fast learning rate.

Despite its theoretical appeal, this viewpoint lacks a crucial ingredient of deep learning in finite DNNs, laying at the heart of their success --feature learning. Here we consider DNNs trained with noisy gradient descent on a large training set and derive a self-consistent Gaussian Process theory accounting forstrongfinite-DNN and feature learning effects.

We can verify that all of above complexity (nearly) matches the corresponding lower bounds.

There are mainly two types of domain adaptation formulas:covariate shift[44, 37, 29, 13] and label shift [27, 2, 1], while we focus on the former in this paper since it appears more natural in recognition tasks and attracts more attention in the literature.