The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.
Contrastive Representation Learning
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IMAGE: A UT Southwestern study discovered the molecular mechanism by which tumors defective in DNA mismatch repair respond to immunotherapy. This illustration depicts how cells use a programmed mismatch repair deficiency-activated. view more
Credit: Illustration by Yipin Wu
DALLAS - Dec. 17, 2020 - DNA that ends up where it doesn t belong in cancer cells can unleash an immune response that makes tumors more susceptible to immunotherapy, the results of two UT Southwestern studies indicate. The findings, published online today in
Cancer Cell, suggest that delivering radiation - which triggers DNA release from cells - before immunotherapy could be an effective way to fight cancers that are challenging to treat.