activity
20172020
most citedLanguage Generation with Recurrent Generative Adversarial Networks without Pre-training

90 citations · 223 across the 12 of their papers we have counts for

collaborators

17 papers

cs.CV202028 cited

DeepFake Detection Based on the Discrepancy Between the Face and its Context

Yuval Nirkin, Lior Wolf, Yosi Keller +1

We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to ad…

cs.CV2020

Structured GANs

Irad Peleg, Lior Wolf

We present Generative Adversarial Networks (GANs), in which the symmetric property of the generated images is controlled. This is obtained through the generator network's architect…

cs.CV20202 cited

Single Image Depth Estimation Trained via Depth from Defocus Cues

Shir Gur, Lior Wolf

Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learn…

cs.LG20205 cited

Unsupervised Learning of the Set of Local Maxima

Lior Wolf, Sagie Benaim, Tomer Galanti

This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown…

cs.CV202018 cited

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

Ori Press, Tomer Galanti, Sagie Benaim +1

We study the problem of learning to map, in an unsupervised way, between domains A and B, such that the samples b in B contain all the information that exists in samples a in A and…

cs.LG20194 cited

Live Face De-Identification in Video

Oran Gafni, Lior Wolf, Yaniv Taigman

We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having…