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Found 8 papers, 5 papers with code
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
On Learned Operator Correction in Inverse Problems
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
In this paper we introduce the first scalable training technique for 3D generative models from 2D data which utilizes an off-the-shelf non-differentiable renderer.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem.
Banach Wasserstein GAN
Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions.
Adversarial Regularizers in Inverse Problems
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.
