Continual semantic segmentation via image and latent space consistency
In this thesis, my continual-learning research process is introduced in detail, including a novel method and two regulators, which contribute to anti-forgetting Result in continual learning in the semantic segmentation area. Firstly a real-time semantic segmentation model called ERFnet is evaluat...
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| 其他作者: | |
| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/154665 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In this thesis, my continual-learning research process is introduced in detail,
including a novel method and two regulators, which contribute to anti-forgetting
Result in continual learning in the semantic segmentation area. Firstly a real-time
semantic segmentation model called ERFnet is evaluated, then based on
this network and Cityscapes dataset, a model-recall method is proposed which
could significantly reduce the catastrophic forgetting which happens in the process
of continual learning in the semantic segmentation area; inspired by mentors,
2 regulators are also conducted which were expected to further improve performance (one regulator is come up by mentors and another is by myself).
A couple of experiments are designed to evaluate the performance of the new
Idea and prediction images for each step is visible. |
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