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20172024
most citedDense xUnit Networks

2 citations · 7 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

Anchored Diffusion for Video Face Reenactment

Idan Kligvasser, Regev Cohen, George Leifman +2

Video generation has drawn significant interest recently, pushing the development of large-scale models capable of producing realistic videos with coherent motion. Due to memory co…

cs.CV2023★ 2 cited

Semi-supervised Quality Evaluation of Colonoscopy Procedures

Idan Kligvasser, George Leifman, Roman Goldenberg +2

Colonoscopy is the standard of care technique for detecting and removing polyps for the prevention of colorectal cancer. Nevertheless, gastroenterologists (GI) routinely miss appro…

cs.CV2023

Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time

G. Leifman, I. Kligvasser, R. Goldenberg +2

Colonoscopy is the most widely used medical technique for preventing Colorectal Cancer, by detecting and removing polyps before they become malignant. Recent studies show that arou…

cs.CV2022

BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning

Idan Kligvasser, Tamar Rott Shaham, Noa Alkobi +1

Training a generative model on a single image has drawn significant attention in recent years. Single image generative methods are designed to learn the internal patch distribution…

cs.CV2021

Sparsity Aware Normalization for GANs

Idan Kligvasser, Tomer Michaeli

Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training. In this paper, we analyze…

cs.CV2018★ 2 cited

Dense xUnit Networks

Idan Kligvasser, Tomer Michaeli

Deep net architectures have constantly evolved over the past few years, leading to significant advancements in a wide array of computer vision tasks. However, besides high accuracy…

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…