Generalized Adaptation for Few-Shot Learning

By Liang Song, Jinlu Liu, Yongqiang Qin. 2019

6 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Micah Goldblum
2. Liam H. Fowl
3. Tom Goldstein
4. Steven Reich
5. Renkun Ni
6. Valeriia Cherepanova
7. Laurent Condat
8. José M. Bioucas-Dias

Citations in the corpus

• Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks (2020, background): “Recently, a number of few-shot benchmarks have been rapidly improved using meta-learning methods (Lee et al., 2019; Song et al., 2019).”
• A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, unknown): “ACC [241] Arxiv’19 Transfer Learning ResNets 62.”