Seminar Details
2022-10-18 (14h) : Unsupervised Learning to Solve Linear Inverse Problems
At Euler building (room a.002)
Organized by Mathematical Engineering
Speaker :
Tachella, Julián
Abstract :
In recent years, deep neural networks have obtained state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. Networks are generally trained with supervised pairs of signals and associated measurements. However, in various imaging problems, we often only have access to incomplete measurements of the underlying signals, thus hindering this learning-based approach. In this talk, I will present a new unsupervised learning framework which overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. I will also present necessary and sufficient conditions for learning without ground truth. Our proposed learning strategy performs as well as fully supervised methods and can handle noisy data. I will show results on various inverse problems, including sparse-view X-ray computed tomography, accelerated magnetic resonance imaging and image inpainting.