Generalized category discovery presents a challenge in a realistic scenario, which requires the model's generalization ability to recognize unlabeled samples from known and unknown categories. This paper revisits the challenge of generalized category discovery through the lens of information maximization (InfoMax) with a probabilistic parametric classifier. Our findings reveal that ensuring independence between known and unknown classes while concurrently assuming a uniform probability distribution across all classes, yields an enlarged margin among known and unknown classes that promotes the model's performance. To achieve the aforementioned independence, we propose a novel InfoMax-based method, Regularized Parametric InfoMax (RPIM), which adopts pseudo labels to supervise unlabeled samples during InfoMax, while proposing a regularization to ensure the quality of the pseudo labels. Additionally, we introduce novel semantic-bias transformation to refine the features from the pre-trained model instead of direct fine-tuning to rescue the computational costs. Extensive experiments on six benchmark datasets validate the effectiveness of our method. RPIM significantly improves the performance regarding unknown classes, surpassing the state-of-the-art method by an average margin of 3.5%.

翻译：广义类别发现在现实场景中提出了一个挑战，它要求模型具备泛化能力以识别来自已知和未知类别的未标记样本。本文通过信息最大化（InfoMax）的视角，结合概率参数化分类器，重新审视广义类别发现的挑战。我们的研究结果表明，在确保已知与未知类别间独立性的同时，假设所有类别服从均匀概率分布，能够扩大已知与未知类别间的间隔，从而提升模型性能。为实现上述独立性，我们提出了一种基于InfoMax的新方法——正则化参数信息最大化（RPIM），该方法在InfoMax过程中采用伪标签监督未标记样本，同时引入正则化以确保伪标签质量。此外，我们提出了新颖的语义偏置变换方法，通过改进预训练模型的特征而非直接微调来降低计算成本。在六个基准数据集上的大量实验验证了本方法的有效性。RPIM在未知类别识别性能上取得显著提升，以平均3.5%的优势超越现有最优方法。