We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, which explores a parameterized family of objective functions, each evaluating a weighted mutual information between the features and the latent labels, subject to supervision constraints from the labeled samples. Our formulation mitigates the class-balance bias encoded in standard information maximization approaches, thereby handling effectively both short-tailed and long-tailed data sets. We report extensive experiments and comparisons demonstrating that our PIM model consistently sets new state-of-the-art performances in GCD across six different datasets, more so when dealing with challenging fine-grained problems.

翻译：我们提出了一种用于广义类别发现（GCD）问题的参数化信息最大化（PIM）模型。具体而言，我们构建了一个双层优化框架，该框架探索了一系列参数化的目标函数，每个函数都基于标注样本的监督约束，计算特征与潜在标签之间的加权互信息。本方法有效缓解了标准信息最大化方法中固有的类别平衡偏差，从而能同时处理短尾和长尾数据集。大量实验与比较表明，我们的PIM模型在六个不同数据集上的GCD任务中持续取得新最优性能，尤其在处理具有挑战性的细粒度问题时表现更为突出。