English(EN) CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

CloSeR框架通过蒸馏闭集教师模型的知识来增强类别发现能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 12:10

研究人员推出了一种名为CloSeR的新型框架，旨在通过利用闭集教师模型的知识来改进广义类别发现（GCD）。该方法解决了当前GCD方法中联合训练可能导致目标冲突和预测偏差的问题。CloSeR采用两阶段过程：首先，训练一个轻量级的、经过领域适应的闭集教师模型；然后，使用统一关系蒸馏（URD）将该教师模型的知识蒸馏到下游GCD任务中。这种方法在多个基准测试中展示了持续的性能提升，并取得了最先进的成果。 AI

影响引入了一种新的类别发现方法，有望提高AI系统识别已知和未知类别的性能。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-08-26 12:10

CloSeR: 来自闭集教师的统一关系蒸馏用于类别发现

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize known classes while discovering coherent novel categories from unlabelled samples. Recent GCD methods…

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CloSeR: 来自闭集教师的统一关系蒸馏用于类别发现

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