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Pre-training datasets are critical for building state-of-the-art machinelearning models, motivating rigorous study on their impact on downstream tasks.In this work, we study the impact of the trade-off between the intra-classdiversity (the number of samples per class) and the inter-class diversity (thenumber of classes) of a supervised pre-training dataset. Empirically, we foundthat with the size of the pre-training dataset fixed, the best downstreamperformance comes with a balance on the intra-/inter-class diversity. Tounderstand the underlying mechanism, we show theoretically that the downstreamperformance depends monotonically on both types of diversity. Notably, ourtheory reveals that the optimal class-to-sample ratio (#classes / #samples perclass) is invariant to the size of the pre-training dataset, which motivates anapplication of predicting the optimal number of pre-training classes. Wedemonstrate the effectiveness of this application by an improvement of around 2points on the downstream tasks when using ImageNet as the pre-training dataset.

On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training