🤖 AI Summary
To address the high training cost and substantial data redundancy in deep learning—hindering simultaneous optimization of generalization and efficiency—this paper proposes a dataset distillation method based on cross-layer attention matching. Our approach leverages multi-layer spatial attention maps from randomly initialized neural network ensembles as discriminative supervision signals, guiding gradient-based synthesis of distilled images. To ensure distribution consistency and class separability, we further introduce multi-scale feature alignment. Crucially, our novel cross-layer attention matching mechanism significantly enhances the fidelity of synthesized data compared to prior methods. The proposed method achieves state-of-the-art performance on CIFAR-10/100, TinyImageNet, and ImageNet-1K: classification accuracy improves by 6.5% on CIFAR-100 and 4.1% on ImageNet-1K. Moreover, we demonstrate its broad applicability in continual learning and neural architecture search, confirming strong generalization beyond standard supervised settings.
📝 Abstract
Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset. Unfortunately, the synthetic data generated by previous methods are not guaranteed to distribute and discriminate as well as the original training data, and they incur significant computational costs. Despite promising results, there still exists a significant performance gap between models trained on condensed synthetic sets and those trained on the whole dataset. In this paper, we address these challenges using efficient Dataset Distillation with Attention Matching (DataDAM), achieving state-of-the-art performance while reducing training costs. Specifically, we learn synthetic images by matching the spatial attention maps of real and synthetic data generated by different layers within a family of randomly initialized neural networks. Our method outperforms the prior methods on several datasets, including CIFAR10/100, TinyImageNet, ImageNet-1K, and subsets of ImageNet-1K across most of the settings, and achieves improvements of up to 6.5% and 4.1% on CIFAR100 and ImageNet-1K, respectively. We also show that our high-quality distilled images have practical benefits for downstream applications, such as continual learning and neural architecture search.
