19 citations · 78 across the 31 of their papers we have counts for
38 papers
Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Anna Rosenberg, John Kennedy, Zohar Keidar +2
Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this we propose the use of ensembles of weak learners…
Identifying Memorization of Diffusion Models through -Laplace Analysis: Estimators, Bounds and Applications
Jonathan Brokman, Itay Gershon, Amit Giloni +4
Diffusion models, today's leading image generative models, estimate the score function, i.e. the gradient of the log probability of (perturbed) data samples, without direct access…
Enhancing Neural Training via a Correlated Dynamics Model
Jonathan Brokman, Roy Betser, Rotem Turjeman +3
As neural networks grow in scale, their training becomes both computationally demanding and rich in dynamics. Amidst the flourishing interest in these training dynamics, we present…
Minimizing Quotient Regularization Model
Chao Wang, Jean-Francois Aujol, Guy Gilboa +1
Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle comp…
Robustifying Point Cloud Networks by Refocusing
Meir Yossef Levi, Guy Gilboa
The ability to cope with out-of-distribution (OOD) corruptions and adversarial attacks is crucial in real-world safety-demanding applications. In this study, we develop a general m…
Additive Class Distinction Maps using Branched-GANs
Elnatan Kadar, Jonathan Brokman, Guy Gilboa
We present a new model, training procedure and architecture to create precise maps of distinction between two classes of images. The objective is to comprehend, in pixel-wise resol…