AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training

• URL: http://arxiv.org/abs/2507.00049v1
• Date: Tue, 24 Jun 2025 22:35:51 GMT
• Title: AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training
• Authors: Feiyang Kang, Nadine Chang, Maying Shen, Marc T. Law, Rafid Mahmood, Ruoxi Jia, Jose M. Alvarez,
• Abstract summary: We introduce Adaptive De-Duplication (AdaDeDup), a novel framework that integrates density-based pruning with model-informed feedback in a cluster-adaptive manner.<n>It significantly outperforms prominent baselines, substantially reduces performance degradation, and achieves near-original model performance while pruning 20% of data.
• Score: 33.01500681857408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The computational burden and inherent redundancy of large-scale datasets challenge the training of contemporary machine learning models. Data pruning offers a solution by selecting smaller, informative subsets, yet existing methods struggle: density-based approaches can be task-agnostic, while model-based techniques may introduce redundancy or prove computationally prohibitive. We introduce Adaptive De-Duplication (AdaDeDup), a novel hybrid framework that synergistically integrates density-based pruning with model-informed feedback in a cluster-adaptive manner. AdaDeDup first partitions data and applies an initial density-based pruning. It then employs a proxy model to evaluate the impact of this initial pruning within each cluster by comparing losses on kept versus pruned samples. This task-aware signal adaptively adjusts cluster-specific pruning thresholds, enabling more aggressive pruning in redundant clusters while preserving critical data in informative ones. Extensive experiments on large-scale object detection benchmarks (Waymo, COCO, nuScenes) using standard models (BEVFormer, Faster R-CNN) demonstrate AdaDeDup's advantages. It significantly outperforms prominent baselines, substantially reduces performance degradation (e.g., over 54% versus random sampling on Waymo), and achieves near-original model performance while pruning 20% of data, highlighting its efficacy in enhancing data efficiency for large-scale model training. Code is open-sourced.

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