Papers › You Only Condense Once: Two Rules for Pruning Condensed Datasets

You Only Condense Once: Two Rules for Pruning Condensed Datasets

21 Oct 2023NeurIPS 2023 11arXiv:2310.14019archive 2025-07-28

Yang He, Lingao Xiao, Joey Tianyi Zhou

Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size of the training dataset, particularly in on-device scenarios. However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited computational resources often preclude the possibility of conducting additional condensation processes. We introduce You Only Condense Once (YOCO) to overcome these limitations. On top of one condensed dataset, YOCO produces smaller condensed datasets with two embarrassingly simple dataset pruning rules: Low LBPE Score and Balanced Construction. YOCO offers two key advantages: 1) it can flexibly resize the dataset to fit varying computational constraints, and 2) it eliminates the need for extra condensation processes, which can be computationally prohibitive. Experiments validate our findings on networks including ConvNet, ResNet and DenseNet, and datasets including CIFAR-10, CIFAR-100 and ImageNet. For example, our YOCO surpassed various dataset condensation and dataset pruning methods on CIFAR-10 with ten Images Per Class (IPC), achieving 6.98-8.89% and 6.31-23.92% accuracy gains, respectively. The code is available at: https://github.com/he-y/you-only-condense-once.

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Tasks

Dataset Condensation

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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConcatenated Skip ConnectionConvolutionDataset PruningDense BlockDense ConnectionsDropoutGlobal Average PoolingKaiming InitializationMax PoolingPruningReLUResidual BlockResidual ConnectionSoftmax

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