Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification

TL;DR

This paper introduces a comprehensive, efficient data pruning method for object re-identification that accurately identifies important, mislabeled, and outlier samples, reducing training costs with minimal accuracy loss.

Contribution

It is the first to adapt and extend data pruning techniques specifically for object re-identification tasks, leveraging logit history for improved sample importance estimation.

Findings

Reduces training data by up to 35% with negligible accuracy loss

Achieves 10x faster importance score estimation

Applicable across multiple ReID datasets

Abstract

Previous studies have demonstrated that not each sample in a dataset is of equal importance during training. Data pruning aims to remove less important or informative samples while still achieving comparable results as training on the original (untruncated) dataset, thereby reducing storage and training costs. However, the majority of data pruning methods are applied to image classification tasks. To our knowledge, this work is the first to explore the feasibility of these pruning methods applied to object re-identification (ReID) tasks, while also presenting a more comprehensive data pruning approach. By fully leveraging the logit history during training, our approach offers a more accurate and comprehensive metric for quantifying sample importance, as well as correcting mislabeled samples and recognizing outliers. Furthermore, our approach is highly efficient, reducing the cost of importance score estimation by 10 times compared to existing methods. Our approach is a plug-and-play, architecture-agnostic framework that can eliminate/reduce 35%, 30%, and 5% of samples/training time on the VeRi, MSMT17 and Market1501 datasets, respectively, with negligible loss in accuracy (< 0.1%). The lists of important, mislabeled, and outlier samples from these ReID datasets are available at https://github.com/Zi-Y/data-pruning-reid.