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Neural Networks for Imaging

From Supervised Learning to Neural Networks
A Brief PyTorch Viewpoint
End-to-End Reconstruction and Imaging Architectures
Self-Supervised Training for Inverse Problems

Noise, Data, and Evaluation

Noise, Model Mismatch, and the Inverse Crime

Generative Models

Variational Autoencoders and GANs
Diffusion Models
Flow Matching Models

Generative Priors for Inverse Problems

Generative Models for Inverse Problems

References

References

Repository
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Index

By Davide Evangelista

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