BECLR: Batch Enhanced Contrastive Few-Shot Learning

• URL: http://arxiv.org/abs/2402.02444v1
• Date: Sun, 4 Feb 2024 10:52:43 GMT
• Title: BECLR: Batch Enhanced Contrastive Few-Shot Learning
• Authors: Stylianos Poulakakis-Daktylidis and Hadi Jamali-Rad
• Abstract summary: Unsupervised few-shot learning aspires to bridge this gap by discarding the reliance on annotations at training time. We propose a novel Dynamic Clustered mEmory (DyCE) module to promote a highly separable latent representation space. We then tackle the, somehow overlooked yet critical, issue of sample bias at the few-shot inference stage.
• Score: 1.450405446885067
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://github.com/stypoumic/BECLR).

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