2 citations · 3 across the 5 of their papers we have counts for
5 papers
SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection
Ido Nitzan Hidekel, Gal lifshitz, Khen Cohen +1
Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs. A central reason is spectral bias, the tendency of neural networks to learn low-frequency…
L-SR1: Learned Symmetric-Rank-One Preconditioning
Gal Lifshitz, Shahar Zuler, Ori Fouks +1
End-to-end deep learning has achieved impressive results but often relies on large labeled datasets, exhibits limited generalization to unseen scenarios, and incurs substantial com…
Systole-Conditioned Generative Cardiac Motion
Shahar Zuler, Gal Lifshitz, Hadar Averbuch-Elor +1
Accurate motion estimation in cardiac computed tomography (CT) imaging is critical for assessing cardiac function and surgical planning. Data-driven methods have become the standar…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Cost Function Unrolling in Unsupervised Optical Flow
Gal Lifshitz, Dan Raviv
Steepest descent algorithms, which are commonly used in deep learning, use the gradient as the descent direction, either as-is or after a direction shift using preconditioning. In…