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Abstract arXiv:2012.09740v1 [cs.LG] 15 Dec 2020
•With a gradient analysis, we show that the temperature is a key parameter to control the strength of penalties on hard negative samples. •We show that there exists a uniformity-tolerance dilemma in contrastive learning, a good choice of tem-perature can compromise the two properties and im-prove the feature quality remarkably.
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