Preventing catastrophic forgetting in continual learning
Continual learning in neural networks has been receiving increased interest due to how prevalent machine learning is in an increasing number of industries. Catastrophic forgetting, which is when a model forgets old tasks upon learning new tasks, is still a major roadblock in allowing neural netwo...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/162924 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Continual learning in neural networks has been receiving increased interest due to
how prevalent machine learning is in an increasing number of industries.
Catastrophic forgetting, which is when a model forgets old tasks upon learning new
tasks, is still a major roadblock in allowing neural networks to be truly life-long
learners. A series of tests were conducted on the effectiveness of using buffers
filled with old training data as a way of mitigating forgetting by training them
alongside new data. The results are that increasing the size of the buffer does help
mitigate forgetting at the cost of increased space used. |
|---|