26 citations · 63 across the 5 of their papers we have counts for
5 papers
Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks
Evyatar Illouz, Eli David, Nathan S. Netanyahu
Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel d…
Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images
Ido Cohen, Eli David, Nathan S. Netanyahu
In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Tradit…
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
Ishai Rosenberg, Guillaume Sicard, Eli David
Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state o…
DeepMimic: Mentor-Student Unlabeled Data Based Training
Itay Mosafi, Eli David, Nathan S. Netanyahu
In this paper, we present a deep neural network (DNN) training approach called the "DeepMimic" training method. Enormous amounts of data are available nowadays for training usage.…
Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data
Ido Faran, Nathan S. Netanyahu, Eli David +4
Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT)…