235 citations · 1.1k across the 53 of their papers we have counts for
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SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Amir Hertz, Or Perel, Raja Giryes +2
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands. We present a spatially adaptive p…
PointGMM: a Neural GMM Network for Point Clouds
Amir Hertz, Rana Hanocka, Raja Giryes +1
Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for th…
CrossNet: Latent Cross-Consistency for Unpaired Image Translation
Omry Sendik, Dani Lischinski, Daniel Cohen-Or
Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation. In particular, it was shown that a wide variety o…
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…