1 citations · 3 across the 10 of their papers we have counts for
12 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…
The Universal Normal Embedding
Chen Tasker, Roy Betser, Eyal Gofer +2
Generative models and vision encoders have largely advanced on separate tracks, optimized for different goals and grounded in different mathematical principles. Yet, they share a f…
Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods
Omer Ben Hayun, Roy Betser, Meir Yossef Levi +2
Following major advances in text and image generation, the video domain has surged, producing highly realistic and controllable sequences. Along with this progress, these models al…
Make it SING: Analyzing Semantic Invariants in Classifiers
Harel Yadid, Meir Yossef Levi, Roy Betser +1
All classifiers, including state-of-the-art vision models, possess invariants, partially rooted in the geometry of their linear mappings. These invariants, which reside in the null…
SCoCCA: Multi-modal Sparse Concept Decomposition via Canonical Correlation Analysis
Ehud Gordon, Meir Yossef Levi, Guy Gilboa
Interpreting the internal reasoning of vision-language models is essential for deploying AI in safety-critical domains. Concept-based explainability provides a human-aligned lens b…
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…
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…
EPiC: Ensemble of Partial Point Clouds for Robust Classification
Meir Yossef Levi, Guy Gilboa
Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this wor…