| Summary: | We explore the impact of training with more diverse
datasets, characterized by the number of unique samples, on the performance of self-supervised learning
(SSL) under a fixed computational budget. Our findings
consistently demonstrate that increasing pretraining
data diversity enhances SSL performance, albeit only
when the distribution distance to the downstream data
is minimal. Notably, even with an exceptionally large
pretraining data diversity achieved through methods like
web crawling or diffusion-generated data, among other
ways, the distribution shift remains a challenge. Our
experiments are comprehensive with seven SSL methods using large-scale datasets such as ImageNet and
YFCC100M amounting to over 200 GPU days. The
code and trained models will be available at https:
//github.com/hammoudhasan/DiversitySSL.
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