OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models

2026-09-03

AI Summary

为解决分类模型中标签移除问题，提出一种基于输出空间重分配的新方法，无需原始数据和特征空间调整，提高计算效率和隐私保护。

Abstract

Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accommodate such changes, classification models must adapt accordingly. Existing solutions, broadly categorized as retraining-based and feature-space-adjustment-based, share common limitations despite their variations, including reliance on access to original data, substantial computational and storage costs, inconsistent results, poor scalability, and degradation of model utility. To address this, we propose a novel approach that leverages statistical redistribution in the output space to approximate the post-removal confidence vectors of a retrained model. Applicable as a modular output filter, our method bypasses the burden of feature-space adjustments or loss-function convergence, alleviating scalability limitations. Furthermore, by requiring only existing labels and prior output confidences, the method potentially mitigates privacy concerns inherent to data-dependent solutions. Extensive experiments demonstrate competitive performance against full retraining, with improvements in computational efficiency and privacy preservation across several classification tasks.

Problem

• Label removal
• classification systems
• evolving taxonomies
• retraining-based
• feature-space-adjustment-based

Innovation

• Output Space Redistribution
• Adaptive Label Removal
• Statistical Redistribute
• Scalability
• Privacy Preservation