327 citations · 726 across the 29 of their papers we have counts for
8 papers · 1 filter
Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples
Nir Zabari, Yedid Hoshen
Semantic segmentation is a key computer vision task that has been actively researched for decades. In recent years, supervised methods have reached unprecedented accuracy, however…
Out-of-Distribution Detection Without Class Labels
Niv Cohen, Ron Abutbul, Yedid Hoshen
Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the n…
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Yoav Levine, Noam Wies, Daniel Jannai +3
Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text into training examples, which are contiguous text segments of sizes processable by the neur…
Image Shape Manipulation from a Single Augmented Training Sample
Yael Vinker, Eliahu Horwitz, Nir Zabari +1
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single ima…
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Aviv Gabbay, Niv Cohen, Yedid Hoshen
Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data. As an alternative approach, recent methods rely on l…
Mean-Shifted Contrastive Loss for Anomaly Detection
Tal Reiss, Yedid Hoshen
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dat…
Scaling-up Disentanglement for Image Translation
Aviv Gabbay, Yedid Hoshen
Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes i…
Membership Inference Attacks are Easier on Difficult Problems
Avital Shafran, Shmuel Peleg, Yedid Hoshen
Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensi…