  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.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning