Name
Abstract | ||
|---|---|---|
How to give agents the ability of continuous learning like human and animals is still a challenge. In the regularized continual learning method OWM, the constraint of the model on the energy compression of the learned task is ignored, which results in the poor performance of the method on the dataset with a large number of learning tasks. In this paper, we propose an energy minimization regularization(EMR) method to constrain the energy of learned tasks, providing enough learning space for the following tasks that are not learned, and increasing the capacity of the model to the number of learning tasks. A large number of experiments show that our method can effectively increase the capacity of the model and reduce the sensitivity of the model to the number of tasks and the size of the network. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9412744 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | ISSN | Citations |
Conference | 1051-4651 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaobin Li | 1 | 15 | 9.25 |
| Lianlei Shan | 2 | 0 | 2.03 |
| Minglong Li | 3 | 0 | 1.01 |
| Weiqiang Wang | 4 | 13 | 8.65 |