10 citations · 19 across the 12 of their papers we have counts for
4 papers · 1 filter
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
Yihao Xue, Siddharth Joshi, Dang Nguyen +1
Recently, multimodal contrastive learning (MMCL) approaches, such as CLIP, have achieved a remarkable success in learning representations that are robust against distribution shift…
Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious Features
Siddharth Joshi, Yu Yang, Yihao Xue +2
Deep neural networks often exploit *spurious* features that are present in the majority of examples within a class during training. This leads to *poor worst-group test accuracy*,…
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Yihao Xue, Siddharth Joshi, Eric Gan +2
Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing represen…
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings
Yihao Xue, Ali Payani, Yu Yang +1
Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments. When obtaining labeled data from the target distribution is e…