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Rethinking Momentum Knowledge Distillation in Online Continual Learning
Nicolas Michel, Maorong Wang, Ling Xiao +1
Online Continual Learning (OCL) addresses the problem of training neural networks on a continuous data stream where multiple classification tasks emerge in sequence. In contrast to…
New metrics for analyzing continual learners
Nicolas Michel, Giovanni Chierchia, Romain Negrel +2
Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, i…
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks
Tetsu Kasanishi, Xueting Wang, Toshihiko Yamasaki
Graph Neural Networks (GNNs) are deep learning models that take graph data as inputs, and they are applied to various tasks such as traffic prediction and molecular property predic…
Investigating Generalization in Neural Networks under Optimally Evolved Training Perturbations
Subhajit Chaudhury, Toshihiko Yamasaki
In this paper, we study the generalization properties of neural networks under input perturbations and show that minimal training data corruption by a few pixel modifications can c…