1 citations · 1 across the 5 of their papers we have counts for
7 papers
SpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging
Shenhav Nadir, Meir Yossef Levi, Eyal Gofer +1
Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliability and robustness concern…
A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP
Hodaya Krakover, Meir Yossef Levi, Eyal Gofer +1
Adversarial attacks pose a challenge to the reliability of deep learning models, motivating effective detection methods. Existing techniques often rely on attack-specific assumptio…
InfoNCE Induces Gaussian Distribution
Roy Betser, Eyal Gofer, Meir Yossef Levi +1
Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) model…
Whitened CLIP as a Likelihood Surrogate of Images and Captions
Roy Betser, Meir Yossef Levi, Guy Gilboa
Likelihood approximations for images are not trivial to compute and can be useful in many applications. We examine the use of Contrastive Language-Image Pre-training (CLIP) to asse…
The Double-Ellipsoid Geometry of CLIP
Meir Yossef Levi, Guy Gilboa
Contrastive Language-Image Pre-Training (CLIP) is highly instrumental in machine learning applications within a large variety of domains. We investigate the geometry of this embedd…
Fast and Simple Explainability for Point Cloud Networks
Meir Yossef Levi, Guy Gilboa
We propose a fast and simple explainable AI (XAI) method for point cloud data. It computes pointwise importance with respect to a trained network downstream task. This allows bette…
DXAI: Explaining Classification by Image Decomposition
Elnatan Kadar, Guy Gilboa
We propose a new way to explain and to visualize neural network classification through a decomposition-based explainable AI (DXAI). Instead of providing an explanation heatmap, our…