Clustering Properties of Self-Supervised Learning
This work addresses the challenge of enhancing self-supervised learning for machine learning practitioners by introducing a novel feedback mechanism, though it appears incremental as it builds on existing SSL frameworks.
The paper tackled the problem of self-supervised learning (SSL) by proposing a method that leverages the clustering properties of SSL representations to improve learning, resulting in models that outperform state-of-the-art SSL methods by a significant margin.
Self-supervised learning (SSL) methods via joint embedding architectures have proven remarkably effective at capturing semantically rich representations with strong clustering properties, magically in the absence of label supervision. Despite this, few of them have explored leveraging these untapped properties to improve themselves. In this paper, we provide an evidence through various metrics that the encoder's output $encoding$ exhibits superior and more stable clustering properties compared to other components. Building on this insight, we propose a novel positive-feedback SSL method, termed Representation Self-Assignment (ReSA), which leverages the model's clustering properties to promote learning in a self-guided manner. Extensive experiments on standard SSL benchmarks reveal that models pretrained with ReSA outperform other state-of-the-art SSL methods by a significant margin. Finally, we analyze how ReSA facilitates better clustering properties, demonstrating that it effectively enhances clustering performance at both fine-grained and coarse-grained levels, shaping representations that are inherently more structured and semantically meaningful.