  Pre-training has been widely adopted in deep learning to improve model
performance, especially when the training data for a target task is limited. In
our work, we seek to understand the implications of this training strategy on
the generalization properties of downstream models. More specifically, we ask
the following question: how do properties of the pre-training distribution
affect the robustness of a fine-tuned model? The properties we explore include
the label space, label semantics, image diversity, data domains, and data
quantity of the pre-training distribution. We find that the primary factor
influencing downstream effective robustness (Taori et al., 2020) is data
quantity, while other factors have limited significance. For example, reducing
the number of ImageNet pre-training classes by 4x while increasing the number
of images per class by 4x (that is, keeping total data quantity fixed) does not
impact the robustness of fine-tuned models. We demonstrate our findings on
pre-training distributions drawn from various natural and synthetic data
sources, primarily using the iWildCam-WILDS distribution shift as a test for
downstream robustness.
