Fig. 4: CH-HNN enhances the performance of class-incremental learning on various datasets.
From: Hybrid neural networks for continual learning inspired by corticohippocampal circuits
a Training protocol diagram for class-incremental learning scenarios. b Correlation matrix of visual samples across 200 classes of the sTiny-ImageNet dataset. Average test accuracy for incrementally learned classes on c sMNIST, d sTiny-ImageNet, and e sCIFAR-100 datasets, presented as means over five random seeds with shaded areas indicating  ±SEM. f Test accuracy for each set of five classes after completing all classes of the sTiny-ImageNet dataset. g Violin plot showing the distribution of test accuracy scores for each set of five classes after learning all tasks in the sCIFAR-100 dataset, with overlaid scatter points highlighting individual data points. h Correlation matrix of the modulation signals generated by ANN across 200 classes in the sTiny-ImageNet dataset.
