6 papers
Image-Adaptive GAN based Reconstruction
Shady Abu Hussein, Tom Tirer, Raja Giryes
In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative advers…
Pruning at Initialization -- A Sketching Perspective
Noga Bar, Raja Giryes
The lottery ticket hypothesis (LTH) has increased attention to pruning neural networks at initialization. We study this problem in the linear setting. We show that finding a sparse…
Multiplicative Reweighting for Robust Neural Network Optimization
Noga Bar, Tomer Koren, Raja Giryes
Neural networks are widespread due to their powerful performance. Yet, they degrade in the presence of noisy labels at training time. Inspired by the setting of learning with exper…
ADIR: Adaptive Diffusion for Image Reconstruction
Shady Abu-Hussein, Tom Tirer, Raja Giryes
Denoising diffusion models have recently achieved remarkable success in image generation, capturing rich information about natural image statistics. This makes them highly promisin…
UDPM: Upsampling Diffusion Probabilistic Models
Shady Abu-Hussein, Raja Giryes
Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds nois…
Denoiser-based projections for 2-D super-resolution multi-reference alignment
Jonathan Shani, Tom Tirer, Raja Giryes +1
We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem…