Are you interested in inverse problems, learning from incomplete data or how to use deep learning for scientific discovery? Come along to our poster #415 in the morning poster session today at #NeurIPS2022!

Our review paper on "Imaging with Equivariant Deep Learning" is now available in final form in ieeexplore.ieee.org/document/10004… !

Our paper has been accepted at the Journal of Machine Learning Research Journal of Machine Learning Research and is now available in final form in jmlr.org/papers/v24/22-… Looking forward to see these ideas applied in more imaging problems! #imaging #deeplearning #unsupervised

📢📢 Release of DeepInverse library 📢📢 After months of intense work, we are releasing the first stable version of DeepInverse deepinv.github.io/deepinv/, a PyTorch library for solving inverse problems with deep learning. with Samuel Hurault, Matthieu Terris and Dongdong Chen A 🧵

Do you need a library for inverse problems, computational imaging, or image reconstruction/restoration? try 'pip install deepinv', then 'import deepinv' is all you need! 🥳 deepinv.github.io #deepinverse

📢Happy to announce the "Deep learning, image analysis, inverse problems, and optimization" workshop on November 27-30 in Lyon, France. perso.ens-lyon.fr/nelly.pustelni… more details below 🧵

🚆On my way to #ICCV2023 Tomorrow I'll be giving an intro to the deepinverse library in the morning deepinv.github.io/deepinv/

Update: Diffusion Posterior Sampling (DPS) is now integrated with deep inverse library (deepinv.github.io). Thanks Hyungjin Chung for the contribution! 'import deepinv as dinv' - one line of code to play with DPS.

The group photo for DIPOpt at ENS Lyon, thanks everyone for coming and sharing ideas and research!

People DeepIng into DeepInv at the DIPopt workshop 🤗

💣The deepinverse library keeps growing! Now you can train your model with the self-supervised loss 'recorrupted2recorrupted' by choosing the R2RLoss

🤗You can now check out some deepinverse denoisers with this interactive huggingface demo huggingface.co/spaces/deepinv… More demos are coming!

"pip install deepinv" to solve your inverse problems🥳🥳

Attending EUSIPCO'24 in Lyon? You can attend a 3-hour tutorial on self-supervised learning for imaging, given by Mike Davies and myself. eusipcolyon.sciencesconf.org/resource/page/… A thread with some of the contents 🧵

Tomorrow we are giving a deepinverse + computational imaging tutorial in Bologna, Italy, with fellow deepinverters Samuel Hurault Matthieu Terris Dongdong Chen site.unibo.it/mathematical-m…

📢New preprint 📢 UNSURE: Unknown Noise level Stein's Unbiased Risk Estimator arxiv.org/abs/2409.01985… Optimal self-supervised losses (SURE, R2R) require exact knowledge of the noise distribution, and alternatives like Noise2Void are suboptimal. There is middle ground🧵

🚀 deepinverse hackathon🚀 We just finished a couple of days working on deepinverse in the beautiful CIRM venue. deepinv.github.io/deepinv/ Immensely grateful to the dream team of contributors that participated in the hackathon! A thread 🧵

Toutes nos félicitations aux deux espoirs des prix science ouverte du logiciel libre de la recherche qui travaillent dans des laboratoires rattachés à CNRS Physique : Julián Tachella (DeepInverse) et Sébastien Weber (PyMoDAQ) 👏 Pour en savoir plus ⤵️ inp.cnrs.fr/fr/cnrsinfo/de…

Not coming back from Marseille empty handed #DeepInverse Julián Tachella Matthieu Terris Dongdong Chen Minh Hai Nguyen

DeepInverse has joined the PyTorch Ecosystem, making imaging with deep learning easier and more reproducible across research and industry. DeepInverse is an open source framework for solving imaging inverse problems in medical imaging, computational photography, remote sensing,