2 citations · 7 across the 7 of their papers we have counts for
9 papers
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…
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
Regev Cohen, Idan Kligvasser, Ehud Rivlin +1
The pursuit of high perceptual quality in image restoration has driven the development of revolutionary generative models, capable of producing results often visually indistinguish…
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
Omer Belhasin, Idan Kligvasser, George Leifman +7
Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. EC…
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…
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…
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…
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…
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…
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…