Search Results for author: Assaf Shocher

Found 15 papers, 9 papers with code

Idempotent Generative Network

1 code implementation2 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.

Rosetta Neurons: Mining the Common Units in a Model Zoo

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).

The Hidden Language of Diffusion Models

1 code implementation1 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.

Bias Detection Image Manipulation

Diverse Video Generation from a Single Video

no code implementations11 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.

Video Generation

Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

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.

Image Generation Image Manipulation

From Discrete to Continuous Convolution Layers

1 code implementation19 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.

Semantic Pyramid for Image Generation

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.

General Classification Image Generation +2

InGAN: Capturing and Retargeting the "DNA" of a Natural Image

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.

Blind Super-Resolution Kernel Estimation using an Internal-GAN

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).

Blind Super-Resolution Super-Resolution

"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors

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.

Image Dehazing Image Segmentation +3

InGAN: Capturing and Remapping the "DNA" of a Natural Image

1 code implementation1 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.

"Zero-Shot" Super-Resolution using Deep Internal Learning

7 code implementations17 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.

Image Compression Image Super-Resolution

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