Importance Driven Continual Learning for Segmentation Across Domains

@article{zgn2020ImportanceDC,
  title={Importance Driven Continual Learning for Segmentation Across Domains},
  author={Sinan {\"O}zg{\"u}r {\"O}zg{\"u}n and Anne-Marie Rickmann and Abhijit Guha Roy and Christian Wachinger},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.00079}
}
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend to forget previously learned tasks when trained on new ones, i.e., they suffer from Catastrophic Forgetting (CF). The objective of Continual Learning (CL) is to alleviate this problem, which is particularly relevant for medical applications, where it may not be feasible to store and access previously used sensitive… 
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