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Found 7 papers, 3 papers with code
Convergent Data-driven Regularizations for CT Reconstruction
The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography (CT).
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields
In contrast to existing discretizations which suffer from a grid bias, we show that a piecewise polynomial discretization better preserves the continuous nature of our problem.
Inverting Gradients - How easy is it to break privacy in federated learning?
The idea of federated learning is to collaboratively train a neural network on a server.
Learning Spectral Regularizations for Linear Inverse Problems
One of the main challenges in linear inverse problems is that a majority of such problems are ill-posed in the sense that the solution does not depend on the data continuously.
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart.
Fast Convex Relaxations using Graph Discretizations
We discuss this methodology in detail and show examples in multi-label segmentation by minimal partitions and stereo estimation, where we demonstrate that the proposed graph discretization can reduce runtime as well as memory consumption of convex relaxations of matching problems by up to a factor of 10.
Inverting Gradients -- How easy is it to break privacy in federated learning?
The idea of federated learning is to collaboratively train a neural network on a server.
