289 citations · 1.7k across the 90 of their papers we have counts for
58 papers · 1 filter
Sample- and Parameter-Efficient Auto-Regressive Image Models
Elad Amrani, Leonid Karlinsky, Alex Bronstein
We introduce XTRA, a vision model pre-trained with a novel auto-regressive objective that significantly enhances both sample and parameter efficiency compared to previous auto-regr…
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems
Yaniv Nemcovsky, Matan Jacoby, Alex M. Bronstein +1
Deep neural networks are known to be susceptible to adversarial perturbations -- small perturbations that alter the output of the network and exist under strict norm limitations. W…
Delta-GAN-Encoder: Encoding Semantic Changes for Explicit Image Editing, using Few Synthetic Samples
Nir Diamant, Nitsan Sandor, Alex M Bronstein
Understating and controlling generative models' latent space is a complex task. In this paper, we propose a novel method for learning to control any desired attribute in a pre-trai…
Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy +14
Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson +2
The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) trai…
Self-Supervised Classification Network
Elad Amrani, Leonid Karlinsky, Alex Bronstein
We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stag…