广义持续类别发现中的类别适应与投影蒸馏
Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
AI总结：
针对GCCD中特征蒸馏限制模型适应新类别的问题，提出结合可学习投影器与辅助类别适应网络的CAMP方法，有效平衡新旧知识学习并在多种场景下取得优异性能。
摘要：
广义持续类别发现（GCCD）致力于从顺序到达、部分标注的数据集中学习，同时发掘新类别。传统方法依赖特征蒸馏来防止遗忘旧知识。然而，该策略限制了模型适应并有效区分新类别的能力。为此，我们提出一种将可学习投影器与特征蒸馏相结合的新技术，从而在不牺牲旧知识的前提下增强模型适应性。由此引发的已学类别分布偏移由辅助类别适应网络加以缓解。我们证明，尽管各组件单独作用时收益有限，但它们的组合——被称为CAMP（类别适应与投影蒸馏）——显著改善了学习新信息与保留旧信息之间的平衡。CAMP在多种GCCD和类增量学习场景中展现出卓越性能。代码可在https://github.com/grypesc/CAMP获取。
英文摘要：
Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model's ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector with feature distillation, thus enhancing model adaptability without sacrificing past knowledge. The resulting distribution shift of the previously learned categories is mitigated with the auxiliary category adaptation network. We demonstrate that while each component offers modest benefits individually, their combination - dubbed CAMP (Category Adaptation Meets Projected distillation) - significantly improves the balance between learning new information and retaining old. CAMP exhibits superior performance across several GCCD and Class Incremental Learning scenarios. The code is available at https://github.com/grypesc/CAMP.
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。
