A simple framework for contrastive learning of visual representations (SimCLR).¶
Chen, T., Kornblith, Norouzi, & Hinton, G. (2020). A simple framework for contrastive learning of visual representations (SimCLR). Proceedings of the 37th International Conference on Machine Learning (ICML), 1597-1607.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Contrast
- Chen, Kornblith, Norouzi, and Hinton (2020), in their SimCLR framework, made this pattern explicit for representation learning: a model is trained to pull positive pairs together and push negative pairs apart in embedding space, using contrast itself as the supervisory signal.
This sourceTrains models to maximize agreement between augmented views of the same image (positive pair) and minimize agreement with views of other images (negative pairs) — supports the claim that contrast itself serves as the supervisory signal for representation learning. (Preprint: arXiv:2002.05709.)
- Chen, Kornblith, Norouzi, and Hinton (2020), in their SimCLR framework, made this pattern explicit for representation learning: a model is trained to pull positive pairs together and push negative pairs apart in embedding space, using contrast itself as the supervisory signal.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:e9f0fb7ec1ac · see in the full table