Maybe a noobs query, but recently I have seen a surge of papers w.r.t contrastive learning (a subset of semi-supervised learning).
Some of the prominent and recent research papers which I read, which detailed this approach are:
- Representation Learning with Contrastive Predictive Coding @ https://arxiv.org/abs/1807.03748
- SimCLR-v1: A Simple Framework for Contrastive Learning of Visual Representations @ https://arxiv.org/abs/2002.05709
- SimCLR-v2: Big Self-Supervised Models are Strong Semi-Supervised Learners @ https://arxiv.org/abs/2006.10029
- MoCo-v1: Momentum Contrast for Unsupervised Visual Representation Learning @ https://arxiv.org/abs/1911.05722
- MoCo-v2: Improved Baselines with Momentum Contrastive Learning @ https://arxiv.org/abs/2003.04297
- PIRL: Self-Supervised Learning of Pretext-Invariant Representations @ https://arxiv.org/abs/1912.01991
Could you guys give a detailed explanation of this approach vs transfer learning and others? Also, why it's gaining traction amongst the ML research community?