BECLR: Batch Enhanced Contrastive Few-Shot Learning

Published: 16 Jan 2024, Last Modified: 21 Apr 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: few-shot classification, unsupervised few-shot learning, deep representation learning
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TL;DR: We introduce BECLR , a novel end-to-end U-FSL approach to: (i) infuse implicit class-level insights within the contrastive pretraining stage and (ii) address the inherent sample bias in the (U)-FSL inference stage, achieving state-of-the-art results.
Abstract: 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
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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 1421