Dynamically growing neural network architecture for lifelong deep learning on the edge
Conventional deep learning models are trained once and deployed. However, models deployed in agents operating in dynamic environments need to constantly acquire new knowledge, while preventing catastrophic forgetting of previous knowledge. This ability is commonly referred to as lifelong learning. I...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146242 |