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 · 11 across the 6 of their papers we have counts for

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cs.CV2023★ 1 cited

Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

Ondrej Bohdal, Yinbing Tian, Yongshuo Zong +5

Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and den…

cs.CV2023★ 2 cited

Amortised Invariance Learning for Contrastive Self-Supervision

Ruchika Chavhan, Henry Gouk, Jan Stuehmer +3

Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances establi…