On Pretraining Data Diversity for Self-Supervised Learning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866907983241281536 |
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| author | Hammoud, Hasan Abed Al Kader Das, Tuhin Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard |
| author_facet | Hammoud, Hasan Abed Al Kader Das, Tuhin Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Pretraining Data Diversity for Self-Supervised Learning Hammoud, Hasan Abed Al Kader Das, Tuhin Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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 |
| title | On Pretraining Data Diversity for Self-Supervised Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.13808 |