Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
4-2022
Abstract
Many deep learning applications, like keyword spotting [1], [2], require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic forgetting, i.e., preserving as much of the old knowledge as possible while learning new tasks. Various techniques, such as regularization, knowledge distillation, and the use of exemplars, have been proposed to resolve this issue. However, prior works primarily focus on the incremental learning step, while ignoring the optimization during the base model training. We hypothesise that a more transferable and generalizable feature representation from the base model would be beneficial to incremental learning.In this work, we adopt multitask learning during base model training to improve the feature generalizability. Specifically, instead of training a single model with all the base classes, we decompose the base classes into multiple subsets and regard each of them as a task. These tasks are trained concurrently and a shared feature extractor is obtained for incremental learning. We evaluate our approach on two datasets under various configurations. The results show that our approach enhances the average incremental learning accuracy by up to 5.5%, which enables more reliable and accurate keyword spotting over time. Moreover, the proposed approach can be combined with many existing techniques and provides additional performance gain.
Keywords
Class Incremental Learning, Continual Learning, Multitask Learning, Keyword Spotting
Discipline
Artificial Intelligence and Robotics | Computer Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, May 22-27
First Page
1
Last Page
5
Identifier
10.1109/ICASSP43922.2022.9746862
Publisher
IEEE
City or Country
Singapore
Citation
MA, Dong; TANG, Chi Ian; and MASCOLO, Cecilia.
Improving feature generalizability with multitask learning in class incremental learning. (2022). Proceedings of 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, May 22-27. 1-5.
Available at: https://ink.library.smu.edu.sg/sis_research/7305
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1109/ICASSP43922.2022.9746862