  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.
