Publications associées à A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

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Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

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Deep Image Restoration: Between Data Fidelity and Learned Priors

Ultrasound Imaging: From Physical Modeling to Deep Learning

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Towards image denoising in the latent space of learning-based compression

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Towards Real-World Super-Resolution using Deep Neural Networks

Robust Generalization via $\alpha$-Mutual Information

Image Compression and Quality Assessment using Convolutional Neural Networks

Robust Generalization via f−Mutual Information

Learning stereo reconstruction with deep neural networks

Ultrasound Imaging: From Physical Modeling to Deep Learning

Image Compression and Quality Assessment using Convolutional Neural Networks

Learning stereo reconstruction with deep neural networks

Deep Image Restoration: Between Data Fidelity and Learned Priors

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Towards image denoising in the latent space of learning-based compression

Robust Generalization via $\alpha$-Mutual Information

Towards Real-World Super-Resolution using Deep Neural Networks

Robust Generalization via f−Mutual Information