most citedLessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

7 citations · 9 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CV2023★ 2 cited

Strong Baselines for Parameter Efficient Few-Shot Fine-tuning

Samyadeep Basu, Daniela Massiceti, Shell Xu Hu +1

Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent work…

cs.LG2022★ 7 cited

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification

Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17

Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…

cs.LG2022

Compressing Features for Learning with Noisy Labels

Yingyi Chen, Shell Xu Hu, Xi Shen +2

Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the d…