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Style Pass
This repository is re-create the results from the paper Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses. It compare's the performance of different contrastive learning loss functions on the CIFAR-100 dataset & on text-pretraining for MS MARCO passage ranking dataset. The following loss functions are compared:
The following image shows the comparison of the loss functions. We can see that the DHEL / DCL loss converges faster than the other loss functions, and in the order mentioned in the paper
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