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Kirkpatrick, J., Pascanu, R., Rabinowitz, N.C., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska Barwinska, A., et al. (2016) Overcoming Catastrophic Forgetting in Neural Networks. Proceedings of the National Academy of Sciences of the United States of America, 114, 3521-3526.
https://doi.org/10.1073/pnas.1611835114

has been cited by the following article:

• TITLE: Incremental Learning Based on Data Translation and Knowledge Distillation

  AUTHORS: Tan Cheng, Jielong Wang

  KEYWORDS: Incremental Domain Learning, Data Translation, Knowledge Distillation, Cat-astrophic Forgetting

  JOURNAL NAME: International Journal of Intelligence Science, Vol.13 No.2, April 21, 2023

  ABSTRACT: Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.