2019

A Broader Study of Cross-Domain Few-Shot Learning

Guo, Yunhui, Codella, Noel C., Karlinsky, Leonid et al.

Understand

Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes.

However, in many applications, collecting data for meta-learning is infeasible or impossible.
This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains.
While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity.

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