AI RESEARCH
Escaping The Big Data Paradigm in Self-Supervised Representation Learning
arXiv CS.CV
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ArXi:2502.18056v2 Announce Type: replace The reliance on large-scale datasets and extensive computational resources has become a major barrier to advancing representation learning in vision, especially in data-scarce domains. In this paper, we address the critical question: Can we escape the big data paradigm in self-supervised representation learning from images? We