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
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我们提出了一种基于已知类数据的无监督新类发现问题的解决方案，其中关键挑战在于将已知类数据中的知识传递到新类的学习中。我们引入了一种基于模型预测的类分布的类关系表示来对新类进行建模，并提出了一个新颖的知识蒸馏框架用于规范新类的学习，通过语义相似性自适应地促进知识传输。通过在多个基准数据集上的广泛实验证实了我们方法的有效性和泛化性，结果表明我们的方法几乎在所有基准数据集上显著优于先前的最先进方法。