arXiv:2502.06434v2 Announce Type: replace-cross Abstract: Dataset pruning (DP) and dataset distillation (DD) fundamentally differ in their outputs: DP selects original image subsets, while DD generates synthetic images. Recently, DD's increasing reliance on original images suggests a convergence of the two directions. To investigate this convergence trend, we propose a unified dataset compression (DC) benchmark. This benchmark reveals an interesting trade-off for soft-label-DD: while soft labels provide valuable information, they can make the distillation process less essential, as distilled i
The increasing scale of AI models and datasets necessitates more efficient compression techniques, making research into unified approaches like dataset pruning and distillation timely.
Better data compression methods can significantly reduce the compute and storage requirements for training large AI models, impacting the accessibility and cost of advanced AI development.
The explicit recognition and benchmarking of convergence between dataset pruning and distillation offers a clearer path towards more efficient and scalable AI training data management.
- · AI developers
- · Cloud service providers
- · Hardware manufacturers (compute efficiency)
- · Academia (research)
- · Organizations with inefficient data pipelines
Reduced data storage and processing costs for AI model development.
Faster training times for large AI models, accelerating research and deployment cycles.
Lower barriers to entry for developing competitive AI, potentially diversifying the AI landscape.
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