Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose Loss Trajectory Correlation (LTC), a novel metric for coreset selection that identifies critical training samples driving generalization. $LTC$ quantifies the alignment between training sample loss trajectories and validation set loss trajectories, enabling the construction of compact, representative subsets. Unlike traditional methods with computational and storage overheads that are infeasible to scale to large datasets, $LTC$ achieves superior efficiency as it can be computed as a byproduct of training. Our results on CIFAR-100 and ImageNet-1k show that $LTC$ consistently achieves accuracy on par with or surpassing state-of-the-art coreset selection methods, with any differences remaining under 1%. LTC also effectively transfers across various architectures, including ResNet, VGG, DenseNet, and Swin Transformer, with minimal performance degradation (<2%). Additionally, LTC offers insights into training dynamics, such as identifying aligned and conflicting sample behaviors, at a fraction of the computational cost of traditional methods. This framework paves the way for scalable coreset selection and efficient dataset optimization.

本研究针对深度学习模型在实时或资源受限环境中的可扩展性挑战，提出了一种新的核心集选择度量——损失轨迹相关性（LTC），能有效识别驱动泛化的关键训练样本。研究结果表明，LTC在CIFAR-100和ImageNet-1k数据集上展现出与现有先进方法相当或更优的准确性，且计算效率高，推动了核心集选择的可扩展性与高效数据集优化。 

发现缪斯：通过损失轨迹识别核心集