Title: Self-Organizing Pathway Expansion for Non-Exemplar Incremental Learning. Abstract: Non-exemplar class-incremental learning aims to recognize both the old and new classes without access to old class samples. The conflict between old and new class optimization is exacerbated since the shared neural pathways can only be differentiated by the incremental samples. To address this problem, we propose a novel self-organizing pathway expansion scheme. Our scheme consists of a class-specific pathway organization strategy that decouples the optimization pathway of different classes to enhance the independence of the feature representation, and a pathway-guided feature optimization mechanism to mitigate the update interference between the old and new classes. Extensive experiments on four datasets demonstrate superior incremental performance, outperforming the state-of-the-art methods by a margin of 1%, 3%, 2% and 2%, respective.