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cs.CV2026
BAM! Bayesian Anything Model: a foundation model for generative computational imaging
Alessio Spagnoletti, Charlesquin Kemajou Mbakam, Jonathan Spence +2
Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls into two camps. Large foundatio…
cs.CV2024
Diffusion-based image inpainting with internal learning
Nicolas Cherel, Andrés Almansa, Yann Gousseau +1
Diffusion models are now the undisputed state-of-the-art for image generation and image restoration. However, they require large amounts of computational power for training and inf…
cs.CV2019
Processsing Simple Geometric Attributes with Autoencoders
Alasdair Newson, Andrés Almansa, Yann Gousseau +1
Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on hig…