SPARC: Learning Spectrally Stable Assignments for Generalized Category Discovery

Generalized Category Discovery (GCD) aims to recognize known categories and discover novel categories in unlabeled data. Despite recent progress driven by strong representation learning, GCD models may rely on appearance cues such as texture, illumination, and background. These cues can distort feature similarity and lead to unstable category assignments. Existing methods mainly enforce consistency between spatially augmented views, while the effect of frequency-domain appearance changes on category assignments remains underexplored. Moreover, prior Fourier-based methods typically use frequency operations for augmentation or feature enhancement, rather than explicitly improving assignment stability. To address this issue, we propose \name, a plug-and-play module for SPectral Assignment Regularization for Categories. SPARC contains two components. Image-level Consistency Regularization (ICR) mixes low-frequency amplitudes from different images while retaining the source phase, and encourages the mixed image to preserve the assignment of the clean image. Token-level Consistency Regularization (TCR) identifies patch tokens that remain stable after spectral filtering, pools them into a Stable Semantic Token (SST), and aligns its assignment with that of the global descriptor. For parametric hosts, SST is additionally used as the readout during inference. Experiments on representative GCD baselines with DINO and DINOv2 demonstrate performance improvements across CIFAR-10/100, ImageNet-100, CUB, Stanford-Cars, and FGVC-Aircraft.