190 citations · 575 across the 59 of their papers we have counts for
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cs.CV2018
Self-supervised Learning of Dense Shape Correspondence
Oshri Halimi, Or Litany, Emanuele Rodolà +2
We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as c…
cs.CV2018
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
Tal Remez, Or Litany, Raja Giryes +1
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising p…
cs.CV2018
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
Or Litany, Daniel Freedman
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is base…