Official code for Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning, ICLR'23.
The iVoro method is based on the idea of Voronoi Diagram subdivision from Computational Geometry.
A. Establish Voronoi Diagram based on base model.
B. Insertion of a new class as a new Voronoi cell enables the minimal intervention to the overall structure.
C. Divide-and-conquer (a classical algorithm for Voronoi construction) efficiently introduce a batch of new classes into the system.
The results of MNIST in 2D space below clearly showed different space subdivision results from conventional fine-tuning, PASS, and different variants of iVoro.
Step 1. Training of the base model, please follow PASS (github).
Step 2. Download the feature files. Google Drive
Go to the directory:
cd MNISTThen run analysis/CIFAR_voro.py in following order:
A. iVoro
B. iVoro-D
C. iVoro-AC/AI
D. iVoro-L
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