Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-019-01219-8
Abstract: We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on… read more here.
Keywords: reconstruction; shape; supervised methods; shape pose ... See more keywords
Optimal Transport for Unsupervised Denoising Learning.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2022.3170155
Abstract: Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an… read more here.
Keywords: microscopy; supervised methods; unsupervised denoising; denoising learning ... See more keywords
Self-supervised denoising of Nyquist-sampled volumetric images via deep learning
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Medical Imaging"
DOI: 10.1117/1.jmi.10.2.024005
Abstract: Abstract. Purpose Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits… read more here.
Keywords: supervised denoising; self supervised; deep learning; denoising nyquist ... See more keywords
Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images
Sign Up to like & getrecommendations! Published in 2022 at "Studies in health technology and informatics"
DOI: 10.3233/shti220263
Abstract: Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference… read more here.
Keywords: supervised self; self supervised; self; comparison supervised ... See more keywords