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Found 6 papers, 0 papers with code
Deep Implicit Surface Point Prediction Networks
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks.
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
SpliceRadar: A Learned Method For Blind Image Forensics
It comprises a novel approach for learning rich filters and for suppressing image-edges.
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).
