GEOMETRY-PRESERVING STRUCTURED PRUNING FOR LONG-TAILED RECOGNITION

Long-tailed recognition models are trained on highly imbalanced data, where tail classes receive limited sample support. Structured channel pruning based on cross-sample importance may discard channels supporting tail-class regions, distorting local geometry and inter-class separation and thereby re-amplifying class bias during compression. To this end, we formulate long-tailed pruning as the preservation of class-local feature-space geometry and propose GB-DCS (Granular-Ball D-Optimal Channel Subspace Selection). GB-DCS uses class-specific granular balls to characterize local representation regions and temporarily masks candidate channels to measure signed deformations in their position, compactness, and separation from other classes. After balancing these responses across classes and frequency groups, GB-DCS applies a multi-kernel D-optimal criterion to retain channel subspaces with complementary geometric responses under a given structural budget. Experiments on moderate-scale CIFAR-100-LT and large-scale ImageNet-LT show improved overall recognition compared with existing methods, with substantial gains on tail classes. GB-DCS provides a geometry-aware framework for balancing model compactness and class-local representation preservation under long-tailed imbalance.