AIBullisharXiv – CS AI · Jun 236/10
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Data Selection Through Iterative Self-Filtering for Vision-Language Settings
Researchers propose a Self-Filtering method that trains CLIP vision-language models on dynamically evolving datasets by iteratively balancing clean samples with diverse data. This bootstrapped approach improves model performance without requiring additional data or pre-trained models, addressing the challenge of training on large-scale noisy datasets.