Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?

Jul 16, 2026

A systematic study compares pretrain-finetuning (PFT) and joint training (JT) paradigms for self-supervised visual representation learning across eight methods and a range of vision tasks, including natural, medical, crisis response, and remote sensing data. The results show that JT improves data and training efficiency and is robust in low-label settings, while PFT tends to be more reliable in specialized domains. The study also analyzes representation quality, robustness, and cross-domain generalization, providing practical guidance for selecting training strategies.

Why it matters: This research offers comprehensive empirical benchmarks and practical insights for choosing between PFT and JT in self-supervised learning, potentially improving efficiency and performance in diverse vision applications.

Full story at: arXiv Computer Vision ↗