10 citations · 14 across the 2 of their papers we have counts for
8 papers
Diverse Video Generation from a Single Video
Niv Haim, Ben Feinstein, Niv Granot +4
GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single vid…
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
Niv Granot, Ben Feinstein, Assaf Shocher +2
Single image generative models perform synthesis and manipulation tasks by capturing the distribution of patches within a single image. The classical (pre Deep Learning) prevailing…
From Discrete to Continuous Convolution Layers
Assaf Shocher, Ben Feinstein, Niv Haim +1
A basic operation in Convolutional Neural Networks (CNNs) is spatial resizing of feature maps. This is done either by strided convolution (donwscaling) or transposed convolution (u…
Semantic Pyramid for Image Generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri +4
We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Inspired by classical image pyramid representations, we c…
Blind Super-Resolution Kernel Estimation using an Internal-GAN
Sefi Bell-Kligler, Assaf Shocher, Michal Irani
Super resolution (SR) methods typically assume that the low-resolution (LR) image was downscaled from the unknown high-resolution (HR) image by a fixed 'ideal' downscaling kernel (…
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors
Yossi Gandelsman, Assaf Shocher, Michal Irani
Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers. For example, image segmentation (separation into foregro…