Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI

Ede S, Baghdadlian S, Weber L, Nguyen A, Zanca D, Samek W, Lapuschkin S (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13480 LNCS

Pages Range: 1-18

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vienna AT

ISBN: 9783031144622

DOI: 10.1007/978-3-031-14463-9_1

Abstract

The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often require large amounts of data available during training time and updates w.r.t. new data are difficult after the training process has been completed. In fact, when new data or tasks arise, previous progress may be lost as neural networks are prone to catastrophic forgetting. Catastrophic forgetting describes the phenomenon when a neural network completely forgets previous knowledge when given new information. We propose a novel training algorithm called Relevance-based Neural Freezing in which we leverage Layer-wise Relevance Propagation in order to retain the information a neural network has already learned in previous tasks when training on new data. The method is evaluated on a range of benchmark datasets as well as more complex data. Our method not only successfully retains the knowledge of old tasks within the neural networks but does so more resource-efficiently than other state-of-the-art solutions.

Authors with CRIS profile

An Nguyen Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab

Involved external institutions

Fraunhofer-Institut für Nachrichtentechnik (Heinrich-Hertz-Institut FhG HHI) DE Germany (DE)

How to cite

APA:

Ede, S., Baghdadlian, S., Weber, L., Nguyen, A., Zanca, D., Samek, W., & Lapuschkin, S. (2022). Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI. In Andreas Holzinger, Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1-18). Vienna, AT: Springer Science and Business Media Deutschland GmbH.

MLA:

Ede, Sami, et al. "Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI." Proceedings of the 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2022, held in conjunction with the 17th International Conference on Availability, Reliability and Security, ARES 2022, Vienna Ed. Andreas Holzinger, Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, Edgar Weippl, Springer Science and Business Media Deutschland GmbH, 2022. 1-18.

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