Concept information
Preferred term
contrastive loss
Definition
- A loss function used in training neural network models which draws representations of similar inputs close to one another, while simultaneously pushing representations of different inputs apart, and is used for tasks such as sentence similarity, semantic textual similarity, and sentence embedding learning. (Based on Moayeri et al., A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes, 2022)
Broader concept
In other languages
-
French
URI
http://data.loterre.fr/ark:/67375/8LP-ZBB710P2-5
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