2 citations · 3 across the 4 of their papers we have counts for
6 papers · 1 filter
Adversarial Feedback Loop
Firas Shama, Roey Mechrez, Alon Shoshan +1
Thanks to their remarkable generative capabilities, GANs have gained great popularity, and are used abundantly in state-of-the-art methods and applications. In a GAN based model, a…
Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks
Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor
One of the key ingredients for successful optimization of modern CNNs is identifying a suitable objective. To date, the objective is fixed a-priori at training time, and any variat…
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Avi Ben-Cohen, Roey Mechrez, Noa Yedidia +1
Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in imag…
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Yochai Blau, Roey Mechrez, Radu Timofte +2
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 ECC…
Maintaining Natural Image Statistics with the Contextual Loss
Roey Mechrez, Itamar Talmi, Firas Shama +1
Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversa…
The Contextual Loss for Image Transformation with Non-Aligned Data
Roey Mechrez, Itamar Talmi, Lihi Zelnik-Manor
Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image. Most of the common lo…