Diffusion Models for Constrained Domains

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

Action editor: Rianne van den Berg.

https://openreview.net/forum?id=xuWTFQ4VGO

#diffusion #denoising #riemannian

IPOL published a new implementation of the algos behind multi-image #denoising #SuperResolution on the Google Pixel phones from a few generations ago:

https://www.ipol.im/pub/art/2023/460/

It's interesting to see these kinds of papers reproduced, even with a few years delay!

Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Mauricio Delbracio, Peyman Milanfar

Action editor: Jia-Bin Huang.

https://openreview.net/forum?id=VmyFF5lL3F

#denoising #restoration #deblurring

Diffusion Models for Constrained Domains

https://openreview.net/forum?id=xuWTFQ4VGO

#diffusion #denoising #riemannian

#stableDiffusion #AI #txt2img
I have just finished this #experiment, in which 310 pictures have been generated with the same seed, only difference is the amount of samples, which increased progressively from 1 to 310.

These generations have then been upscaled using the realest-general-x4v3 upscaler model and #ComfyUI as software.

This the result:
https://youtu.be/hU4MyESWnm0

#AIart #denoising #Realesr #Dreamshaper

Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role ...

Rishi Sonthalia, Raj Rao Nadakuditi

https://openreview.net/forum?id=FdMWtpVT1I

#denoising #generalization #shrinkage

Zhou et al. propose a noise-aware #deeplearning model 'NAFSA-Net' for #underwater acoustic signal #denoising which adopts #encoder & stacked fullband-subband attention (FSA) blocks to capture both global and fine-grained local dependencies of features
https://ieeexplore.ieee.org/abstract/document/10064313

Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role of Training Noise

https://openreview.net/forum?id=FdMWtpVT1I

#denoising #generalization #shrinkage

New post on the ISPGroup website, by Benoit Brummer and Christophe De Vleeschouwer, UCLouvain, on "On the Importance of Denoising When Learning to Compress Images". #Image #Compression
#Denoising #DeepLearning
https://ispgroup.gitlab.io/research/on-the-importance-of-denoising-when-learning-to-compress-images/

Read our latest #realtime #denoising research!

"Weighted À-Trous Linear Regression (WALR) for Real-Time Diffuse Indirect Lighting Denoising" - a proposal for a new regression-based method for denoising images from path-traced #globalillumination with few rays/pixel #raytracing

https://gpuopen.com/download/publications/GPUOpen2022_WALR.pdf?utm_source=twitter&utm_medium=social&utm_campaign=arrg

Jesus Christ - "Our two-stage distillation approach is able to generate realistic images using only 1 to 4 #denoising steps on various tasks. Compared to the standard classifier-free guided diffusion models, ***we reduce the total number of sampling steps by at least 20X***."

Model coming soon for #StableDiffusion!

https://twitter.com/EMostaque/status/1598131202044866560/photo/1

(Hopefully it won't be insanely slow per-step, or glitch-prone)

#AIArt

#arxivfeed

"Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data"
https://arxiv.org/abs/2211.05918v1

#denoising #SINDy #nonlinear #dynamics #data #ODE #modelling