  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.
