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Found 15 papers, 9 papers with code
Idempotent Generative Network
We define the target manifold as the set of all instances that $f$ maps to themselves.
Stochastic positional embeddings improve masked image modeling
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images.
Rosetta Neurons: Mining the Common Units in a Model Zoo
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
In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model.
Diverse Video Generation from a Single Video
GANs are able to perform generation and manipulation tasks, trained on a single video.
Diverse Generation from a Single Video Made Possible
GANs are able to perform generation and manipulation tasks, trained on a single video.
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
Recently, however, Single Image GANs were introduced both as a superior solution for such manipulation tasks, but also for remarkable novel generative tasks.
From Discrete to Continuous Convolution Layers
We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.
Semantic Pyramid for Image Generation
We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks.
InGAN: Capturing and Retargeting the "DNA" of a Natural Image
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
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
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors
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.
InGAN: Capturing and Remapping the "DNA" of a Natural Image
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.
âZero-Shotâ Super-Resolution Using Deep Internal Learning
On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.
"Zero-Shot" Super-Resolution using Deep Internal Learning
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
