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, astronomical imaging, microscopy and more. DeepInverse makes imaging with deep learning easy and is developed by a passionate community of researchers, practitioners and engineers. 🔗 Learn more & explore how to get involved: hubs.la/Q03RKyHJ0 #PyTorch #OpenSourceAI #DeepInverse #Imaging #DeepLearning

Not coming back from Marseille empty handed #DeepInverse @TachellaJulian @MatthieuTerris @ddongchen @minhhai712

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

🚀 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 🧵

📢New preprint 📢 UNSURE: Unknown Noise level Stein's Unbiased Risk Estimator arxiv.org/abs/2409.01985v1 Optimal self-supervised losses (SURE, R2R) require exact knowledge of the noise distribution, and alternatives like Noise2Void are suboptimal. There is middle ground🧵

Tomorrow we are giving a deepinverse computational imaging tutorial in Bologna, Italy, with fellow deepinverters @HuraultSamuel @MatthieuTerris @ddongchen site.unibo.it/mathematical-m…

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… A thread with some of the contents 🧵

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

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

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

People DeepIng into DeepInv at the DIPopt workshop 🤗

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

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.

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

📢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.pust… more details below 🧵

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

📢📢 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 @HuraultSamuel, @MatthieuTerris and @ddongchen A 🧵

Our paper has been accepted at the Journal of Machine Learning Research @JmlrOrg and is now available in final form in jmlr.org/papers/v24/22-0315.… Looking forward to see these ideas applied in more imaging problems! #imaging #deeplearning #unsupervised

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

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!