On Pretraining Data Diversity for Self-Supervised Learning
This work addresses the problem of optimizing data diversity for self-supervised learning in computer vision, but it is incremental as it builds on existing SSL methods and datasets.
The study investigated how increasing pretraining data diversity affects self-supervised learning performance under a fixed computational budget, finding that it enhances performance only when the distribution distance to downstream data is minimal, with experiments covering seven SSL methods and large-scale datasets like ImageNet and YFCC100M over 200 GPU days.
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. Code and trained models are available at https://github.com/hammoudhasan/DiversitySSL