289 citations · 1.7k across the 90 of their papers we have counts for
9 papers · 2 filters
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Sivan Doveh, Eli Schwartz, Chao Xue +4
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typically, in FSL a model is trained on a dataset consisting of many small tasks (meta-tasks) and learns to adapt to…
Localization with Limited Annotation for Chest X-rays
Eyal Rozenberg, Daniel Freedman, Alex Bronstein
Localization of an object within an image is a common task in medical imaging. Learning to localize or detect objects typically requires the collection of data which has been label…
CAT: Compression-Aware Training for bandwidth reduction
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii +3
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving visual processing tasks. One of the major obstacles hindering the ubiquitous u…
Baby steps towards few-shot learning with multiple semantics
Eli Schwartz, Leonid Karlinsky, Rogerio Feris +2
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerabl…
Learning to Detect and Retrieve Objects from Unlabeled Videos
Elad Amrani, Rami Ben-Ari, Tal Hakim +1
Learning an object detector or retrieval requires a large data set with manual annotations. Such data sets are expensive and time consuming to create and therefore difficult to obt…
Feature Map Transform Coding for Energy-Efficient CNN Inference
Brian Chmiel, Chaim Baskin, Ron Banner +5
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a variety of tasks in computer vision and beyond. One of the major obstacles hindering the ubiquitous use…