  Humans have a unique ability to learn new representations from just a handful
of examples with little to no supervision. Deep learning models, however,
require an abundance of data and supervision to perform at a satisfactory
level. Unsupervised few-shot learning (U-FSL) is the pursuit of bridging this
gap between machines and humans. Inspired by the capacity of graph neural
networks (GNNs) in discovering complex inter-sample relationships, we propose a
novel self-attention based message passing contrastive learning approach
(coined as SAMP-CLR) for U-FSL pre-training. We also propose an optimal
transport (OT) based fine-tuning strategy (we call OpT-Tune) to efficiently
induce task awareness into our novel end-to-end unsupervised few-shot
classification framework (SAMPTransfer). Our extensive experimental results
corroborate the efficacy of SAMPTransfer in a variety of downstream few-shot
classification scenarios, setting a new state-of-the-art for U-FSL on both
miniImagenet and tieredImagenet benchmarks, offering up to 7%+ and 5%+
improvements, respectively. Our further investigations also confirm that
SAMPTransfer remains on-par with some supervised baselines on miniImagenet and
outperforms all existing U-FSL baselines in a challenging cross-domain
scenario. Our code can be found in our GitHub repository at
https://github.com/ojss/SAMPTransfer/.
