Self-supervised Deep Learning for Image Recovery - SIAM-IS Virtual Seminar

Self-supervised Deep Learning for Image Recovery - SIAM-IS Virtual Seminar

Society for Industrial and Applied Mathematics via YouTube Direct link

Dataset-dependence of supervised learning methods

8 of 16

8 of 16

Dataset-dependence of supervised learning methods

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Classroom Contents

Self-supervised Deep Learning for Image Recovery - SIAM-IS Virtual Seminar

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  1. 1 Intro
  2. 2 Inverse problem: Image deblurring
  3. 3 Image recovery/reconstruction
  4. 4 Regularization methods for image recovery
  5. 5 Non-local self-similarity prior of images
  6. 6 Deep learning for linear inverse problem
  7. 7 An example: Unrolling half-quadratic splitting scheme
  8. 8 Dataset-dependence of supervised learning methods
  9. 9 A self-supervised approach to general image recovery proble
  10. 10 Part 1: Data augmentation & Self-supervised loss function
  11. 11 Data augmentation via Bernoulli random sampling
  12. 12 Recap: Self-supervised loss function
  13. 13 Testing with Bayesian neural network
  14. 14 Experiments on removing Gaussian white noise from images
  15. 15 Stability of training
  16. 16 Visual inspection

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