Digital Library[ Search Result ]
Continual Learning using Memory-Efficient Parameter Generation
Hyung-Wook Lim, Han-Eol Kang, Dong-Wan Choi
http://doi.org/10.5626/JOK.2024.51.8.747
Continual Learning with Parameter Generation shows remarkable stability in retaining knowledge from previous tasks. However, it suffers from a gradual decline in parameter generation performance due to its lack of adaptability to new tasks. Furthermore, the difficulty in predetermining the optimal size of the parameter generation model (meta-model) can lead to memory efficiency issues. To address these limitations, this paper proposed two novel techniques. Firstly, the Chunk Save & Replay (CSR) technique selectively stored and replayed vulnerable parts of the generative neural network, maintaining diversity in the parameter generation model while efficiently utilizing memory. Secondly, the Automatically Growing GAN (AG-GAN) technique automatically expanded the memory of the parameter generation model based on learning tasks, enabling effective memory utilization in resource-constrained environments. Experimental results demonstrated that these proposed techniques significantly reduced memory usage while minimizing performance degradation. Moreover, their ability to recover from deteriorated network performance was observed. This research presents new approaches to overcoming limitations of parameter generation-based continual learning, facilitating the implementation of more effective and efficient continual learning systems.
Re-Generation of Models via Generative Adversarial Networks and Bayesian Neural Networks for Task-Incremental Learning
http://doi.org/10.5626/JOK.2022.49.12.1115
In contrast to the human ability of continual learning, deep learning models have considerable difficulty maintaining their original performance when the model learns a series of incrementally arriving tasks. In this paper, we propose ParameterGAN, a novel task-incremental learning approach based on model synthesis. The proposed method leverages adversarial generative learning to regenerate neural networks themselves which have a parameter distribution similar to that of a pre-trained Bayesian network. Also, using pseudo-rehearsal methods, ParameterGAN enables continual learning by regenerating the networks of all previous tasks without catastrophic forgetting. Our experiment showed that the accuracy of the synthetic model composed of regenerated parameters was comparable to that of the pre-trained model, and the proposed method outperformed the other SOTA methods in the comparative experiments using the popular task-incremental learning benchmarks Split-MNIST and Permuted-MNIST.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr