  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%.
