🔥Telegram İfşa

“Typical priors: ‘This image looks natural.’ FiRE🔥priors: ‘This image survives degradation + restoration.’ Our CVPR 2025 paper shows how fixed points of restoration models can serve as powerful priors for inverse problems—because real images shouldn’t vanish when restored.

🚀 Do you want to learn about self-supervised learning for inverse problems? ▶️ Check out the 3-hour tutorial presented by Mike Davies and myself, covering recent advances in a unified and simplified framework! youtube.com/playlist?list=…

⚡ Last days to apply for this exciting project to make significant contributions to space-based telescopes like Euclid and JWST using statistics and machine learning! 🪐 Deadline: 1st April! ⏳

🚢🚢 deepinv v0.3.0 is here, with many new features! 🚢 🚢 Our passionate team of contributors keeps shipping more exciting tools! Deepinverse (deepinv.github.io) is a library for solving imaging inverse problems with deep learning.

I'll be in Singapore this week for #ICLR2025, presenting "UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate" Ping me if you are around too!