Class-relation Knowledge Distillation for Novel Class Discovery

TL;DR

This paper introduces a novel class relation-based knowledge distillation method to improve the discovery of new classes without supervision, outperforming previous methods across multiple datasets.

Contribution

It proposes a class relation representation and a learnable regularization scheme for knowledge transfer in novel class discovery, addressing the limitations of existing approaches.

Findings

Outperforms state-of-the-art methods on multiple benchmarks

Effectively transfers knowledge from known to novel classes

Demonstrates robustness across diverse datasets

Abstract

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}.