Corpus ID: 209862603

The troublesome kernel: why deep learning for inverse problems is typically unstable

@article{Gottschling2020TheTK,
  title={The troublesome kernel: why deep learning for inverse problems is typically unstable},
  author={Nina Gottschling and Vegard Antun and B. Adcock and A. Hansen},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.01258}
}
  • Nina Gottschling, Vegard Antun, +1 author A. Hansen
  • Published 2020
  • Computer Science
  • ArXiv
  • There is overwhelming empirical evidence that Deep Learning (DL) leads to unstable methods in applications ranging from image classification and computer vision to voice recognition and automated diagnosis in medicine. Recently, a similar instability phenomenon has been discovered when DL is used to solve certain problems in computational science, namely, inverse problems in imaging. In this paper we present a comprehensive mathematical analysis explaining the many facets of the instability… CONTINUE READING
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