Three last-layer coefficients derived from gradient interference sources predict the forgetting rank order of past classes in rehearsal-based CIL, with self-induced interference as the strongest predictor.
Catastrophic forgetting, rehearsal and pseudorehearsal
3 Pith papers cite this work. Polarity classification is still indexing.
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A data-free class-incremental learning method for gesture recognition using prototype-guided pseudo feature replay with four components that achieves 11.8% and 12.8% mean global accuracy gains on SHREC 2017 3D and EgoGesture 3D datasets.
Pseudo-rehearsal method with cGAN-generated old-concept samples, balanced online recall, and concept contrastive loss for class-incremental learning on MNIST, Fashion-MNIST and SVHN.
citing papers explorer
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Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning
Three last-layer coefficients derived from gradient interference sources predict the forgetting rank order of past classes in rehearsal-based CIL, with self-induced interference as the strongest predictor.
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Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay
A data-free class-incremental learning method for gesture recognition using prototype-guided pseudo feature replay with four components that achieves 11.8% and 12.8% mean global accuracy gains on SHREC 2017 3D and EgoGesture 3D datasets.
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Incremental Concept Learning via Online Generative Memory Recall
Pseudo-rehearsal method with cGAN-generated old-concept samples, balanced online recall, and concept contrastive loss for class-incremental learning on MNIST, Fashion-MNIST and SVHN.