Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning, where only part of the category labels are assigned to certain training samples. Previous methods generally employ naive contrastive learning or unsupervised clustering scheme for all the samples. Nevertheless, they usually ignore the inherent critical information within the historical predictions of the model being trained. Specifically, we empirically reveal that a significant number of salient unlabeled samples yield consistent historical predictions corresponding to their ground truth category. From this observation, we propose a Memory Consistency guided Divide-and-conquer Learning framework (MCDL). In this framework, we introduce two memory banks to record historical prediction of unlabeled data, which are exploited to measure the credibility of each sample in terms of its prediction consistency. With the guidance of credibility, we can design a divide-and-conquer learning strategy to fully utilize the discriminative information of unlabeled data while alleviating the negative influence of noisy labels. Extensive experimental results on multiple benchmarks demonstrate the generality and superiority of our method, where our method outperforms state-of-the-art models by a large margin on both seen and unseen classes of the generic image recognition and challenging semantic shift settings (i.e.,with +8.4% gain on CUB and +8.1% on Standford Cars).

翻译：广义类别发现（GCD）旨在解决更现实且更具挑战性的半监督学习场景，其中仅有部分类别标签被分配给特定训练样本。以往方法通常对所有样本采用简单的对比学习或无监督聚类方案，却往往忽略了被训练模型历史预测中蕴含的内在关键信息。具体而言，我们通过实验揭示：大量显著未标注样本会产生与其真实类别一致的历史预测。基于这一发现，我们提出记忆一致性引导的分治学习框架（MCDL）。在该框架中，我们引入两个记忆库记录未标注数据的历史预测，用于评估各样本基于预测一致性的可信度。在可信度指导下，我们设计分治学习策略，既能充分利用未标注数据的判别信息，又能缓解噪声标签的负面影响。在多个基准数据集上的广泛实验表明，本方法具有通用性与优越性：在通用图像识别与具有挑战性的语义偏移场景（即CUB数据集上提升+8.4%，斯坦福汽车数据集上提升+8.1%）中，本方法在已知和未知类别上均大幅超越现有最优模型。