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Rethinking Large-scale Dataset Compression: Shifting Focus From Labels to Images by Xiao, Lingao; Liu, Songhua; He, Yang; Wang, Xinchao is a scholarly article available to read on EtoBox.

What is Rethinking Large-scale Dataset Compression: Shifting Focus From Labels to Images about?

Dataset distillation and dataset pruning are two prominent techniques for compressing datasets to improve computational and storage efficiency. Despite their overlapping objectives, these approaches are rarely compared directly. Even within each field, the evaluation protocols are inconsistent across various methods, which complicates fair comparisons and hinders reproducibility. Considering these limitations, we introduce in this paper a benchmark that equitably evaluates methodologies across both distillation and pruning literatures. Notably, our benchmark reveals that in the mainstream dataset distillation setting for large-scale datasets, which heavily rely on soft labels from pre-trained models, even randomly selected subsets can achieve surprisingly competitive performance. This finding suggests that an overemphasis on soft labels may be diverting attention from the intrinsic value of the image data, while also imposing additional burdens in terms of generation, storage, and application. To address these issues, we propose a new framework for dataset compression, termed Prune, Combine, and Augment (PCA), which focuses on leveraging image data exclusively, relies solely on har

Author

Xiao, Lingao; Liu, Songhua; He, Yang; Wang, Xinchao

Published

2025

Language

EN