SUPERFORMER : continual learning superposition method for text classification

Zeman, Marko (author), Faganeli Pucer, Jana (author), Kononenko, Igor (author), Bosnić, Zoran (author)

One of the biggest challenges in continual learning domains is the tendency of machine learning models to forget previously learned information over time. While overcoming this issue, the existing approaches often exploit large amounts of additional memory and apply model forgetting mitigation mechanisms which substantially prolong the training process. Therefore, we propose a novel SUPERFORMER method that alleviates model forgetting, while spending negligible additional memory and time. We tackle the continual learning challenges in a learning scenario, where we learn different tasks in a sequential order. We compare our method against several prominent continual learning methods, i.e., EWC, SI, MAS, GEM, PSP, etc. on a set of text classification tasks. We achieve the best average performance in terms of AUROC and AUPRC (0.7% and 0.9% gain on average, respectively) and the lowest training time among all the methods of comparison. On average, our method reduces the total training time by a factor of 5.4-8.5 in comparison to similarly performing methods. In terms of the additional memory, our method is on par with the most memory-efficient approaches.

• Language: English
• Keywords: deep learning, continual learning, superposition, transformers
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: Str. 418-436
• Numbering: Vol. 161
• PID: 20.500.12556/RUL-144861
• UDC: 004.8
• ISSN on article: 0893-6080
• DOI: 10.1016/j.neunet.2023.01.040
• COBISS.SI-ID: 141099267
• Publication date in RUL: 17.03.2023

Record is a part of a journal

• Title: Neural networks
• Shortened title: Neural netw.
• Publisher: Elsevier
• ISSN: 0893-6080
• COBISS.SI-ID: 26011904

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: globoko učenje, nenehno učenje, superpozicija, transformerji

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0209
• Name: Umetna inteligenca in inteligentni sistemi