Self-Supervised Class-Cognizant Few-Shot Classification
TL
TL;DR
This paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on a downstream classification task and extends a recent study on adopting contrastive learning for self-supervised pre-training by incorporating class-level cognizance through iterative clustering and re-ranking and expanding the contrastive optimization loss to account for it.
Abstract
Unsupervised learning is argued to be the dark matter of human intelligence 1. 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.
References28 items
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Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
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Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
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Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
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