The great success of deep learning relies heavily on ever-increasing amounts of training data, which incurs enormous computational and infrastructural costs. This raises crucial questions: Does all training data contribute equally to a model's performance? How much does each individual training sample or sub-training set affect the model's generalization, and how can we construct the smallest proxy training set without significantly sacrificing performance? To address these questions, we propose dataset pruning, an optimization-based sample selection method that (1) examines the influence of removing particular training samples on the model's generalization ability with high computational efficiency and theoretical guarantees, and (2) constructs the smallest subset of training data that yields a strictly constrained generalization gap. The empirically observed generalization gap achieved by dataset pruning is largely consistent with our theoretical expectations. To estimate each sample's influence, we develop an SGD-Influence method that tracks parameter changes during stochastic gradient descent, effectively overcoming the convexity and optimality assumptions required by traditional influence function approaches. Complementing this, our distributed discrete optimization partitions the dataset into manageable buckets, allowing for efficient sample selection without sacrificing quality. Extensive experiments on three datasets of varying scale and complexity demonstrate that our approach not only aligns well with theoretical expectations but also outperforms state-of-the-art methods. Notably, compared to our previous work, the proposed method achieves a 61.26% reduction in computational cost while delivering higher accuracy.
Yang et al. (Thu,) studied this question.