Prior to Memory: Bayesian Assignment Projection for Continual Generalized Category Discovery

Continual generalized category discovery (C-GCD) requires discovering novel classes from sequentially arriving unlabeled data containing both old and novel classes while retaining recognition of previously learned classes. However, parameter updates under fixed pseudo-labels do not automatically revise sample assignments, allowing initial assignment errors to potentially persist in category memories. We propose Bayesian Assignment Projection (BAP), which jointly refines old/new separation and assignments among novel classes before memory construction. Assuming that the scale of per-class sample counts transfers from base to novel classes, BAP uses base-class counts to construct Bayesian predictions of novel-class sizes, converts predictive intervals into marginal assignment constraints, and optimizes assignments by alternating regularized information projection with auxiliary Gaussian posterior estimation. Under the stated conditions, our theoretical analysis establishes the uniqueness of the fixed-cost projection solution and guarantees a non-increasing auxiliary objective under exact alternating updates within a fixed stage. With identical feature representations, data streams, and Gaussian memory models, experiments over multiple initializations show that BAP outperforms the baseline in mean final overall recognition accuracy on all four standard benchmarks, with an average gain of 2.68 percentage points across datasets.