1 code implementation • 13 Jul 2023 • Alexander Krull, Hector Basevi, Benjamin Salmon, Andre Zeug, Franziska Müller, Samuel Tonks, Leela Muppala, Ales Leonardis
This new perspective allows us to make three contributions: We present a new strategy for self-supervised denoising, We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image.
no code implementations • NeurIPS 2018 • Yilun Du, Zhijian Liu, Hector Basevi, Ales Leonardis, Bill Freeman, Josh Tenenbaum, Jiajun Wu
We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples.