DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning

In this paper, we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Dif-ferent from the existing methods that continuously learn the feature space from data streams, we propose a novel compute-and-align paradigm for the OTFCIL. It first com-putes an optimal geometry, i.e., the class prototype distri-bution, for classifying existing classes and updates it when new classes emerge, and then trains a DNN model by aligning its feature space to the optimal geometry. To this end, we develop a novel Dynamic Neural Collapse (DNC) algorithm to compute and update the optimal geometry. The DNC ex-pands the geometry when new classes emerge without loss of the geometry optimality and guarantees the drift distance of old class prototypes with an explicit upper bound. On this basis, we propose a novel DYnamic feature space Self-OrganizatioN (DYSON) method containing three ma-jor components, including 1) a feature extractor, 2) a Dy-namic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier de-rived from the DNC geometry. Experimental comparison results on four benchmark datasets, including CIFAR10, CI-FAR100, CUB200, and CoRe50, demonstrate the efficiency and superiority of the DYSON method. The source code is released at https://github.com/isCDX2IDYSON.