Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining

• URL: http://arxiv.org/abs/2503.08805v1
• Date: Tue, 11 Mar 2025 18:34:12 GMT
• Title: Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining
• Authors: Mikey Shechter, Yair Carmon,
• Abstract summary: Filter Like You Test (FLYT) is a method for curating large-scale vision-language datasets.<n>FLYT trains a scoring model that learns to weigh each example using gradient signals from downstream tasks training sets.<n>Mixing-FLYT (M-FLYT) takes the per-example scores generated by different scoring methods and learns to unify them into a single score.
• Score: 17.402771370806384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Filter Like You Test (FLYT), a method for curating large-scale vision-language datasets that learns the usefulness of each data point as a pretraining example. FLYT trains a scoring model that learns to weigh each example using gradient signals from downstream tasks training sets. Using the same training methodology, we develop Mixing-FLYT (M-FLYT), which takes the per-example scores generated by different scoring methods and learns to unify them into a single score. Our training methodology naturally produces a distribution over the training examples, which we leverage through Soft Cap Sampling (SCS), a strategy for obtaining a filtered pretraining dataset from per-example probabilities that samples examples while preventing over-representation through a repetition penalty. Using all three methods, we achieve 40.1% ImageNet zero-shot accuracy on the DataComp medium scale filtering benchmark, a 1.9% absolute accuracy increase over all previous results and a 5.5% increase over results that -- like us -- use only public resources.

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