  Foundation models like CLIP are trained on hundreds of millions of samples
and effortlessly generalize to new tasks and inputs. Out of the box, CLIP shows
stellar zero-shot and few-shot capabilities on a wide range of
out-of-distribution (OOD) benchmarks, which prior works attribute mainly to
today's large and comprehensive training dataset (like LAION). However, it is
questionable how meaningful terms like out-of-distribution generalization are
for CLIP as it seems likely that web-scale datasets like LAION simply contain
many samples that are similar to common OOD benchmarks originally designed for
ImageNet. To test this hypothesis, we retrain CLIP on pruned LAION splits that
replicate ImageNet's train-test similarity with respect to common OOD
benchmarks. While we observe a performance drop on some benchmarks,
surprisingly, CLIP's overall performance remains high. This shows that high
train-test similarity is insufficient to explain CLIP's OOD performance, and
other properties of the training data must drive CLIP to learn more
generalizable representations. Additionally, by pruning data points that are
dissimilar to the OOD benchmarks, we uncover a 100M split of LAION
($\frac{1}{4}$th of its original size) on which CLIP can be trained to match
its original OOD performance.
