PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity
This addresses the need for efficient data selection in dynamic contexts for deep learning applications, though it is incremental as it builds on existing data selection methods.
The paper tackles the problem of selecting key training examples incrementally from a continuous data stream, proposing PEAKS, which outperforms existing strategies and shows better performance returns than random selection as data size grows on real-world datasets.
As deep learning continues to be driven by ever-larger datasets, understanding which examples are most important for generalization has become a critical question. While progress in data selection continues, emerging applications require studying this problem in dynamic contexts. To bridge this gap, we pose the Incremental Data Selection (IDS) problem, where examples arrive as a continuous stream, and need to be selected without access to the full data source. In this setting, the learner must incrementally build a training dataset of predefined size while simultaneously learning the underlying task. We find that in IDS, the impact of a new sample on the model state depends fundamentally on both its geometric relationship in the feature space and its prediction error. Leveraging this insight, we propose PEAKS (Prediction Error Anchored by Kernel Similarity), an efficient data selection method tailored for IDS. Our comprehensive evaluations demonstrate that PEAKS consistently outperforms existing selection strategies. Furthermore, PEAKS yields increasingly better performance returns than random selection as training data size grows on real-world datasets. The code is available at https://github.com/BurakGurbuz97/PEAKS.