Deep Inversion, Autoencoders for Learned Regularization (...) - Brune - Workshop 3 - CEB T1 2019
Matthias Ehrhardt - Bilevel Learning for Inverse Problems
SANE 2015: Pablo Sprechmann (NYU) on Deep Learning for Solving Inverse Problems
Learning to Solve PDE-constrained Inverse Problems with Graph Networks | ICML 2022
NN - 16 - L2 Regularization / Weight Decay (Theory + @PyTorch code)
Deep Learning Foundations: Mahdi Soltanolkotabi's talk on Feature learning & inverse problems
Stéphane Mallat: "Deep Generative Networks as Inverse Problems"
Regularization - Dropout
Deep Unfolding with Normalizing Flow Priors for Inverse Problems - ICASSP 2022
Data-driven regularisation for solving inverse problems - Carola-Bibiane Schönlieb, Turing/Cambridge
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Deep Generative Models And Unsupervised Methods For Inverse Problems
Martin Genzel: Solving Inverse Problems With Deep Neural Networks - Robustness Included?
Inverse Problems 5: Convergence of Tikhonov regularization in small noise limit
Luca Ratti (University of Helsinki) - Deep neural networks for inverse problems
Inverse Problems and Invertibility in Deep Learning: Marius Aasan (University of Oslo)
Deep Learning Lecture on Autoencoders
Variational Autoencoders | Generative AI Animated
Solving Bayesian Inverse Problems via Variational Autoencoders
Deep Learning for Inverse Problems in Medical Imaging
