1 code implementation • 2 Nov 2023 • Assaf Shocher, Amil Dravid, Yossi Gandelsman, Inbar Mosseri, Michael Rubinstein, Alexei A. Efros
We define the target manifold as the set of all instances that $f$ maps to themselves.
no code implementations • 31 Jul 2023 • Amir Bar, Florian Bordes, Assaf Shocher, Mahmoud Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann Lecun
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images.
no code implementations • ICCV 2023 • Amil Dravid, Yossi Gandelsman, Alexei A. Efros, Assaf Shocher
In this paper, we demonstrate the existence of common features we call "Rosetta Neurons" across a range of models with different architectures, different tasks (generative and discriminative), and different types of supervision (class-supervised, text-supervised, self-supervised).
1 code implementation • 1 Jun 2023 • Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, Michal Irani, Inbar Mosseri, Lior Wolf
In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model.
no code implementations • 11 May 2022 • Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel, Michal Irani
GANs are able to perform generation and manipulation tasks, trained on a single video.
no code implementations • 17 Sep 2021 • Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel, Michal Irani
GANs are able to perform generation and manipulation tasks, trained on a single video.
2 code implementations • CVPR 2022 • Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, Michal Irani
Recently, however, Single Image GANs were introduced both as a superior solution for such manipulation tasks, but also for remarkable novel generative tasks.
1 code implementation • 19 Jun 2020 • Assaf Shocher, Ben Feinstein, Niv Haim, Michal Irani
We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.
2 code implementations • CVPR 2020 • Assaf Shocher, Yossi Gandelsman, Inbar Mosseri, Michal Yarom, Michal Irani, William T. Freeman, Tali Dekel
We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks.
no code implementations • ICCV 2019 • Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani
In this paper we propose an "Internal GAN" (InGAN) -- an image-specific GAN -- which trains on a single input image and learns its internal distribution of patches.
4 code implementations • NeurIPS 2019 • 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 (e. g. Bicubic downscaling).
Ranked #5 on Blind Super-Resolution on DIV2KRK - 2x upscaling
1 code implementation • Computer Vision Foundation 2018 • Yossi Gandelsman, Assaf Shocher, Michal Irani
It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image.
1 code implementation • 1 Dec 2018 • Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani
In this paper we propose an "Internal GAN" (InGAN) - an image-specific GAN - which trains on a single input image and learns its internal distribution of patches.
no code implementations • CVPR 2018 • Assaf Shocher, Nadav Cohen, Michal Irani
On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.
7 code implementations • 17 Dec 2017 • Assaf Shocher, Nadav Cohen, Michal Irani
On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.
Ranked #46 on Image Super-Resolution on BSD100 - 4x upscaling