对比多种模型对旋节分解事件分类效果，MobileViT和NAT表现最优。
Comparison of Machine Learning Approaches for Classifying Spinodal Events
- 用深度学习模型对比分类旋节分解数据，测试了CNN、MobileViT等
- NAT与MobileViT准确率达94.65%，AUC达0.98，优于CNN
- 适合关注材料相变分析与模型性能对比的研究者
本文对比了多种机器学习模型在旋节分解数据集上的分类性能。评估了先进模型（MobileViT、NAT、EfficientNet、CNN）及多种集成方法（多数投票、AdaBoost）。同时探索了在转换色彩空间下的数据表现。结果表明，NAT与MobileViT在训练与测试数据上均表现最佳，准确率分别为94.65%和94.20%，AUC为0.98，F1分数达0.94，显著优于早期的CNN模型（准确率88.44%，AUC 0.95，F1 0.88）。文中还讨论了表现最佳模型的失败案例。
原文摘要 · Abstract (English)
In this work, we compare the performance of deep learning models for classifying the spinodal dataset. We evaluate state-of-the-art models (MobileViT, NAT, EfficientNet, CNN), alongside several ensemble models (majority voting, AdaBoost). Additionally, we explore the dataset in a transformed color space. Our findings show that NAT and MobileViT outperform other models, achieving the highest metrics-accuracy, AUC, and F1 score on both training and testing data (NAT: 94.65, 0.98, 0.94; MobileViT: 94.20, 0.98, 0.94), surpassing the earlier CNN model (88.44, 0.95, 0.88). We also discuss failure cases for the top performing models.
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