Anchored Residual Integration with Structure-regularized Adaptation for Generalized Category Discovery

Generalized category discovery uses labeled examples from known classes to recognize both known and novel categories in unlabeled data. In fine-grained settings, closely related novel categories often share substantial semantic structure, with distinguishing cues expressed as subtle deviations in the representation. We identify a geometric mechanism in cosine classification that can weaken these cues: relative to a semantic anchor derived from known classes, residual directional similarity is scaled by the product of the sample and prototype residual magnitudes. Consequently, useful discriminative information may contribute weakly to classification even when it remains encoded in the representation. We propose ARISE, a two-stage framework combining structure-regularized adaptation with anchored residual integration. First, ARISE adapts pretrained representations under known-class supervision while constraining changes in local relations through a momentum reference encoder. It then constructs a semantic anchor for each novel prototype and applies square-root reweighting to mitigate residual-magnitude attenuation while retaining sensitivity to residual strength. A novelty-and-ambiguity gate selectively applies the correction during novel-category learning and joint inference. Across six benchmarks, ARISE achieves an average overall accuracy of 71.9% and the highest overall accuracy among the compared methods on all four fine-grained benchmarks.