Finding the Muses: Identifying Coresets through Loss Trajectories
This addresses the problem of efficient dataset optimization for deep learning practitioners in resource-constrained scenarios, representing a novel method for a known bottleneck.
The paper tackles the scalability challenge in deep learning by proposing Loss Trajectory Correlation (LTC), a metric for coreset selection that identifies critical training samples, achieving accuracy on par with or surpassing state-of-the-art methods with differences under 1% on CIFAR-100 and ImageNet-1k.
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.