ClearGCD: Mitigating Shortcut Learning for Robust Generalized Category Discovery

In open-world scenarios, Generalized Category Discovery (GCD) requires identifying both known and novel categories in unlabeled data, but existing methods often face prototype confusion from shortcut learning, weakening generalization and causing forgetting of known classes. We propose ClearGCD, a framework that suppresses reliance on non-semantic cues through two complementary mechanisms: Semantic View Alignment (SVA), generating strong augmentations via cross-class patch replacement while enforcing semantic consistency with weak augmentations, and Shortcut Suppression Regularization (SSR), maintaining an adaptive prototype bank that aligns known classes and separates potential novel ones. ClearGCD can be seamlessly integrated into parametric GCD methods and consistently outperforms state-of-the-art methods across multiple benchmarks.