Search Results for author: Yochai Blau

Found 8 papers, 2 papers with code

It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems

no code implementations NeurIPS 2021 Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin

In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.

Denoising

Detecting Deficient Coverage in Colonoscopies

no code implementations23 Jan 2020 Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin

Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.

Depth Estimation

The effectiveness of layer-by-layer training using the information bottleneck principle

no code implementations ICLR 2019 Adar Elad, Doron Haviv, Yochai Blau, Tomer Michaeli

The recently proposed information bottleneck (IB) theory of deep nets suggests that during training, each layer attempts to maximize its mutual information (MI) with the target labels (so as to allow good prediction accuracy), while minimizing its MI with the input (leading to effective compression and thus good generalization).

Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

no code implementations23 Jan 2019 Yochai Blau, Tomer Michaeli

Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (e. g., low MSE or high SSIM) at any given bit rate.

SSIM

The 2018 PIRM Challenge on Perceptual Image Super-resolution

8 code implementations20 Sep 2018 Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, Lihi Zelnik-Manor

This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018.

Image Restoration Image Super-Resolution +1

The Perception-Distortion Tradeoff

1 code implementation CVPR 2018 Yochai Blau, Tomer Michaeli

Image restoration algorithms are typically evaluated by some distortion measure (e. g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality.

Image Restoration SSIM +1

Non-Redundant Spectral Dimensionality Reduction

no code implementations11 Dec 2016 Yochai Blau, Tomer Michaeli

Our approach relies on replacing the orthogonality constraints underlying those methods by unpredictability constraints.

Dimensionality Reduction

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