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FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection

arXiv – CS AI|Jin Cui, Boran Zhao, Jiajun Xu, Jiaqi Guo, Shuo Guan, Pengju Ren|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose FAST, a new DNN-free framework for coreset selection that compresses large datasets into representative subsets for training deep neural networks. The method uses frequency-domain distribution matching and achieves 9.12% average accuracy improvement while reducing power consumption by 96.57% compared to existing methods.

Key Takeaways

→FAST introduces the first DNN-free distribution-matching framework for coreset selection, avoiding model-specific architectural bias.
→The method employs Characteristic Function Distance (CFD) in frequency domain to capture full distributional information more accurately than traditional metrics.
→A new Attenuated Phase-Decoupled CFD addresses the "vanishing phase gradient" issue in medium and high-frequency regions.
→Progressive Discrepancy-Aware Sampling strategy schedules frequency selection from low to high for better convergence and global structure preservation.
→FAST achieves 9.12% average accuracy gain, 96.57% power consumption reduction, and 2.2x speedup compared to baseline coreset methods.

#machine-learning #deep-learning #coreset-selection #energy-efficiency #optimization #neural-networks #research #performance #frequency-domain

Read Original →via arXiv – CS AI

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