We tackle the problem of novel class discovery, which aims to learn novel classes without supervision based on labeled data from known classes. A key challenge lies in transferring the knowledge in the known-class data to the learning of novel classes. Previous methods mainly focus on building a shared representation space for knowledge transfer and often ignore modeling class relations. To address this, we introduce a class relation representation for the novel classes based on the predicted class distribution of a model trained on known classes. Empirically, we find that such class relation becomes less informative during typical discovery training. To prevent such information loss, we propose a novel knowledge distillation framework, which utilizes our class-relation representation to regularize the learning of novel classes. In addition, to enable a flexible knowledge distillation scheme for each data point in novel classes, we develop a learnable weighting function for the regularization, which adaptively promotes knowledge transfer based on the semantic similarity between the novel and known classes. To validate the effectiveness and generalization of our method, we conduct extensive experiments on multiple benchmarks, including CIFAR100, Stanford Cars, CUB, and FGVC-Aircraft datasets. Our results demonstrate that the proposed method outperforms the previous state-of-the-art methods by a significant margin on almost all benchmarks. Code is available at \href{https://github.com/kleinzcy/Cr-KD-NCD}{here}.

翻译：摘要：我们研究新类别发现问题，旨在基于已知类别的标注数据，在无监督条件下学习新类别。关键挑战在于将已知类别数据中的知识迁移至新类别的学习过程。现有方法主要侧重于构建共享表示空间以实现知识迁移，但常忽略对类别关系的建模。为此，我们基于已知类别训练模型输出的预测类别分布，引入针对新类别的类关系表示。实验发现，在典型发现训练过程中，此类关系的信息量会逐渐减弱。为防止信息丢失，我们提出一种新颖的知识蒸馏框架，利用类关系表示约束新类别的学习过程。进一步地，为实现对新类别数据点灵活的知识蒸馏策略，我们开发了可学习的加权函数用于约束机制，该函数根据新类别与已知类别的语义相似度自适应地促进知识迁移。为验证方法的有效性与泛化性，我们在CIFAR100、Stanford Cars、CUB和FGVC-Aircraft等多个基准数据集上开展广泛实验。结果表明，所提方法在几乎所有基准测试中均显著超越先前最优方法。代码已发布于\href{https://github.com/kleinzcy/Cr-KD-NCD}{此处}。