1 citations · 3 across the 10 of their papers we have counts for
10 papers · 1 filter
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…
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…