English(EN) SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing

新的SmoothOperator方法提高了开放集识别的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了一种名为SmoothOperator (SmoothOP) 的新方法，以改进机器学习模型中的开放集识别 (OSR)。OSR 使模型能够正确分类已知类别，同时识别和拒绝未知类别。SmoothOP 通过根据样本的“突出度”（衡量其类别在竞争类别中有多么清晰地脱颖而出）动态调整标签平滑系数来增强现有的球面表示学习技术。这种方法带来了性能的提升，在 Semantic Shift Benchmark 上的 AUROC、OSCR 和闭集准确率方面提高了高达 4.7%。 AI

影响增强了模型区分已知和未知数据的能力，可能改进现实世界中的AI应用。

排序理由详细介绍开放集识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Thiru Thillai Nadarasar Bahavan, Yu Xia, Sachith Seneviratne, Saman Halgamuge · 2026-10-02 04:00

SmoothOperator：通过调制标签平滑增强细粒度开放集识别的表示

arXiv:2610.00851v1 Announce Type: new Abstract: Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes. A key challenge in OSR lies in the inability to model the unbounded distribution of unknown classes d…

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报道来源 [1]

SmoothOperator：通过调制标签平滑增强细粒度开放集识别的表示

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