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T-MARS : Improving Visual Representations by Circumventing Text Feature Learning
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T-MARS: Improving Visual Representations by Circumventing Text Feature Learning T-MARS : Improving Visual Representations by Circumventing Text Feature Learning Pratyush Maini * 1 Sachin Goyal * 1 Zachary Lipton 1 Zico Kolter 1,2 Aditi Raghunathan 1 1 Carnegie Mellon University 2 Bosch Center for AI arXiv GitHub Summary TLDR We propose an algorithm to filter web datasets used for training CLIP in order to learn better visual representations, and achieve state-of-art zeroshot accuracy on vision tasks. Goal 1. Vision language models like CLIP are trained on web-crawled image caption pairs. 2. We aim to filter these web-datasets for better visual representation learning and improve zero-shot performance. 3. Filtering out bad samples will allow allocating computation resources to useful datapoints. A Look at the LAION Dataset 1. Our analysis shows an interesting observation: a large fraction of ima...
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