| Summary: | 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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