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：广义类别发现旨在解决更现实且更具挑战性的半监督学习场景，其中仅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朴素对比学习或无监督聚类方案，然而它们往往忽略了模型训练过程中历史预测所蕴含的内在关键信息。具体而言，我们通过实验发现大量显著未标注样本的历史预测与其真实类别标签保持高度一致。基于这一观察，我们提出记忆一致性引导的分治学习框架。在该框架中引入两个记忆库记录未标注数据的历史预测，并据此通过预测一致性度量各样本的可信度。在可信度引导下，我们可设计分治学习策略，在充分利用未标注数据判别性信息的同时减轻噪声标签的负面影响。多个基准数据集的广泛实验结果表明，本方法具有普适性与优越性，在通用图像识别及具有挑战性的语义偏移设置（即在CUB上获得+8.4%增益，在斯坦福汽车数据集上获得+8.1%增益）中，对已知和未知类别的性能均大幅超越当前最优模型。