Phase transition frequency predicts ResNet accuracy in training

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员发现了一种新的指标“相变频率”，可以预测ResNet模型在训练过程中的测试准确率。该指标统计离散的类别可分性跳跃次数，在CIFAR-10和CIFAR-100等标准基准测试中与准确率呈强负相关。然而，在分布偏移压力下，如在TinyImageNet和CIFAR-10-C基准测试中观察到的那样，这种预测能力会减弱。进一步分析表明，虽然相变频率是训练曲线信号中的一个强预测因子，但对于受压数据集，其他方法更有效。 AI

影响这项研究提供了一种新颖的训练时间指标，有助于优化模型开发并及早发现潜在的准确率问题。

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Phase transition frequency predicts ResNet accuracy in training

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arXiv cs.AI TIER_1 English(EN) · Arunan J · 2026-09-07 04:00

ResNets中相变频率作为测试准确性的训练时间预测因子

arXiv:2609.05194v1 Announce Type: cross Abstract: The number of discrete class-separability jumps observed during ResNet finetuning is examined empirically as a predictor of final test accuracy. Across 75 experiments spanning four benchmarks (CIFAR-10, CIFAR-100, TinyImageNet, an…

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ResNets中相变频率作为测试准确性的训练时间预测因子

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