Study Finds Joint Training Beats Pretrain-Finetuning for Self-Supervised Vision
New research compares 8 SSL methods across 4 domains—JT wins on efficiency.
Researchers systematically compared two paradigms for self-supervised visual representation learning: pretraining followed by finetuning (PFT) versus joint training (JT), where self-supervised and supervised losses are optimized simultaneously. Testing eight SSL methods on natural, medical, crisis response, and remote sensing data, they found JT consistently improves data and training efficiency and is robust in low-label settings, while PFT remains more reliable for specialized domains. The study provides a comprehensive benchmark for hybrid semi-supervised learning.
- Joint training (JT) outperforms pretrain-finetuning (PFT) in data efficiency, often achieving comparable results with 50–90% fewer labeled examples.
- PFT remains more robust for specialized domains like medical imaging and remote sensing where pretrained features are less transferable.
- Experiments covered 8 SSL methods across 4 domains (natural, medical, crisis, remote sensing) with labeled data percentages from 1% to 100%.
Why It Matters
This benchmark helps practitioners choose the right training paradigm, saving time and labeled data in real-world vision applications.