997 citations · 2.4k across the 65 of their papers we have counts for
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cs.CV2017
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
Idan Kligvasser, Tamar Rott Shaham, Tomer Michaeli
In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very dee…
cs.CV2017★ 997 cited
The Perception-Distortion Tradeoff
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. I…
stat.ML2017★ 5 cited
Joint auto-encoders: a flexible multi-task learning framework
Baruch Epstein, Ron Meir, Tomer Michaeli
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availab…