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Provable Contrastive Continual Learning
In this work, we fill this gap by establishing theoretical performance guarantees, which reveal how the performance of the model is bounded by training losses of previous tasks in the contrastive continual learning framework.
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification
Our solution secures the 1st place in the MedFMC challenge.
OTMatch: Improving Semi-Supervised Learning with Optimal Transport
Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data.
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