contrastive loss
Contrastive loss is a method used in machine learning to encourage the model to differentiate between similar and dissimilar pairs of data points. It aims to minimize the distance or dissimilarity between similar pairs, while maximizing the distance or dissimilarity between dissimilar pairs. This loss function helps the model learn to encode useful representations and improve its ability to perform tasks like similarity estimation or classification.
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