4 papers
Generative Low-Shot Network Expansion
Adi Hayat, Mark Kliger, Shachar Fleishman +1
Conventional deep learning classifiers are static in the sense that they are trained on a predefined set of classes and learning to classify a novel class typically requires re-tra…
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish +3
Polygonal meshes provide an efficient representation for 3D shapes. They explicitly capture both shape surface and topology, and leverage non-uniformity to represent large flat reg…
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
Rana Hanocka, Noa Fish, Zhenhua Wang +3
The process of aligning a pair of shapes is a fundamental operation in computer graphics. Traditional approaches rely heavily on matching corresponding points or features to guide…
Novelty Detection with GAN
Mark Kliger, Shachar Fleishman
The ability of a classifier to recognize unknown inputs is important for many classification-based systems. We discuss the problem of simultaneous classification and novelty detect…