OSR：分类模型中自适应标签移除的输出空间重分配
OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models
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中文总结 AI 辅助
针对分类系统中标签移除的问题，提出输出空间重分配（OSR）方法，作为模块化输出过滤器绕过特征空间调整，在多项分类任务中实现与全重训练相当的性能，提升了计算效率与隐私保护。
中文摘要 AI 辅助
在具有不断演变分类体系的分类系统中，标签移除频繁发生，此时类别必须动态更新或删除，分类模型需相应适配。现有解决方案大致分为基于重训练和基于特征空间调整两类，虽各有不同，但存在共同局限，包括依赖原始数据访问、计算与存储成本高、结果不一致、可扩展性差及模型效用下降。为解决此问题，我们提出一种利用输出空间统计重分配的新方法，以近似重训练模型移除标签后的置信向量。作为模块化输出过滤器，该方法可绕过特征空间调整或损失函数收敛的负担，缓解可扩展性限制；且仅需现有标签和先验输出置信度，有望减轻依赖数据方案固有的隐私问题。大量实验表明，该方法与全重训练相比性能相当，在多项分类任务中计算效率和隐私保护均有提升。
英文摘要
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.
发表机构
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
