mlsanityData to 14 Oct 2026

Self-Supervised Class-Cognizant Few-Shot Classification

arXiv 2202.08149 ↗First posted 15 Feb 2022cs.CV cs.AI cs.LGcited by 2

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Abstract (arXiv)

Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classification task. To this aim, we extend a recent study on adopting contrastive learning for self-supervised pre-training by incorporating class-level cognizance through iterative clustering and re-ranking and by expanding the contrastive optimization loss to account for it. To our knowledge, our experimentation both in standard and cross-domain scenarios demonstrate that we set a new state-of-the-art (SoTA) in (5-way, 1 and 5-shot) settings of standard mini-ImageNet benchmark as well as the (5-way, 5 and 20-shot) settings of cross-domain CDFSL benchmark. Our code and experimentation can be found in our GitHub repository: https://github.com/ojss/c3lr.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 15 Feb 2022.

Builds on

22 of 28 references.

References · 22

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10 more
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  3. 2018Unsupervised Meta-Learning For Few-Shot Image Classificationcited by 75
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2024BECLR: Batch Enhanced Contrastive Few-Shot Learningcited by 6
  2. 2022Self-Attention Message Passing for Contrastive Few-Shot Learningcited by 4