  Learning quickly from very few labeled samples is a fundamental attribute
that separates machines and humans in the era of deep representation learning.
Unsupervised few-shot learning (U-FSL) aspires to bridge this gap by discarding
the reliance on annotations at training time. Intrigued by the success of
contrastive learning approaches in the realm of U-FSL, we structurally approach
their shortcomings in both pretraining and downstream inference stages. We
propose a novel Dynamic Clustered mEmory (DyCE) module to promote a highly
separable latent representation space for enhancing positive sampling at the
pretraining phase and infusing implicit class-level insights into unsupervised
contrastive learning. We then tackle the, somehow overlooked yet critical,
issue of sample bias at the few-shot inference stage. We propose an iterative
Optimal Transport-based distribution Alignment (OpTA) strategy and demonstrate
that it efficiently addresses the problem, especially in low-shot scenarios
where FSL approaches suffer the most from sample bias. We later on discuss that
DyCE and OpTA are two intertwined pieces of a novel end-to-end approach (we
coin as BECLR), constructively magnifying each other's impact. We then present
a suite of extensive quantitative and qualitative experimentation to
corroborate that BECLR sets a new state-of-the-art across ALL existing U-FSL
benchmarks (to the best of our knowledge), and significantly outperforms the
best of the current baselines (codebase available at:
https://github.com/stypoumic/BECLR).
