On the Importance of Distractors for Few-Shot Classification

1 code implementation • ICCV 2021 • Rajshekhar Das, Yu-Xiong Wang, JoséM. F. Moura

An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations.

Classification Contrastive Learning