Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
This work addresses the challenge of reducing the massive data requirements for CLIP pre-training, which is significant for researchers and practitioners in computer vision and NLP seeking more efficient and scalable models.
The paper tackles the problem of data efficiency in Contrastive Language-Image Pre-training (CLIP) by proposing a theoretically rigorous data selection method that prioritizes data quality over quantity, achieving over 2.7x and 1.4x accuracy improvements on ImageNet and its shifted versions compared to baselines.
Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such models require a massive amount of pre-training data. Improving the quality of the pre-training data has been shown to be much more effective in improving CLIP's performance than increasing its volume. Nevertheless, finding small subsets of training data that provably generalize the best has remained an open question. In this work, we propose the first theoretically rigorous data selection method for CLIP. We show that subsets that closely preserve the cross-covariance of the images and captions of the full data provably achieve a superior generalization performance. Our extensive experiments on ConceptualCaptions3M and ConceptualCaptions12M demonstrate that subsets found by \method\ achieve over 2.7x and 1.4x the accuracy of the next best baseline on ImageNet and its shifted versions. Moreover, we show that our subsets obtain 1.5x the average accuracy across 11 downstream datasets, of the next best baseline. The code is available at: https://github.com/BigML-CS-UCLA/clipcov-data-efficient-clip.