基于代理锚点与极值理论驱动的连续学习方法用于广义类别发现
Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery
AI总结：
本文提出将极值理论与代理锚点结合，通过包含概率函数和EVT损失提升新类别分离与表示学习，并利用模型缩减、经验回放和知识蒸馏缓解类别高估与灾难性遗忘，在连续广义类别发现中取得领先性能。
摘要：
连续广义类别发现已在文献中被提出并研究，作为一种旨在持续发现和学习传入数据批次中的新类别，同时避免对先前已学习类别发生灾难性遗忘的方法。应对这一挑战的一个关键组成部分是模型分离新样本的能力，其中极值理论（EVT）已被有效采用。在这项工作中，我们提出了一种新方法，将EVT与代理锚点相结合，利用包含概率函数围绕代理定义边界，从而能够拒绝未知样本。此外，我们引入了一种新的基于EVT的损失函数来增强所学表示，在类似设置下相比其他深度度量学习方法取得了更优的性能。利用推导出的概率函数，新样本能够与先前已知类别有效分离。然而，在这些新样本中进行类别发现有时会高估新类别的数量。为缓解这一问题，我们提出了一种新的基于EVT的方法来减小模型规模并丢弃冗余代理。我们还在连续学习阶段引入了经验回放和知识蒸馏机制，以防止灾难性遗忘。实验结果表明，我们提出的方法在连续广义类别发现场景中优于当前最先进的方法。
英文摘要：
Continual generalized category discovery has been introduced and studied in the literature as a method that aims to continuously discover and learn novel categories in incoming data batches while avoiding catastrophic forgetting of previously learned categories. A key component in addressing this challenge is the model's ability to separate novel samples, where Extreme Value Theory (EVT) has been effectively employed. In this work, we propose a novel method that integrates EVT with proxy anchors to define boundaries around proxies using a probability of inclusion function, enabling the rejection of unknown samples. Additionally, we introduce a novel EVT-based loss function to enhance the learned representation, achieving superior performance compared to other deep-metric learning methods in similar settings. Using the derived probability functions, novel samples are effectively separated from previously known categories. However, category discovery within these novel samples can sometimes overestimate the number of new categories. To mitigate this issue, we propose a novel EVT-based approach to reduce the model size and discard redundant proxies. We also incorporate experience replay and knowledge distillation mechanisms during the continual learning stage to prevent catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms state-of-the-art methods in continual generalized category discovery scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
