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06/13/2023
Compositionally Equivariant Representation Learning
Deep learning models often need sufficient supervision (i.e. labelled da...
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08/06/2022
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Learning disentangled representations requires either supervision or the...
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06/29/2022
vMFNet: Compositionality Meets Domain-generalised Segmentation
Training medical image segmentation models usually requires a large amou...
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08/26/2021
A Tutorial on Learning Disentangled Representations in the Imaging Domain
Disentangled representation learning has been proposed as an approach to...
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07/04/2021
Controllable cardiac synthesis via disentangled anatomy arithmetic
Acquiring annotated data at scale with rare diseases or conditions remai...
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06/24/2021
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Generalising deep models to new data from new centres (termed here domai...
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08/27/2020
Metrics for Exposing the Biases of Content-Style Disentanglement
Recent state-of-the-art semi- and un-supervised solutions for challengin...
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08/26/2020
Disentangled Representations for Domain-generalized Cardiac Segmentation
Robust cardiac image segmentation is still an open challenge due to the ...
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04/18/2020
A Deep Learning Approach to Object Affordance Segmentation
Learning to understand and infer object functionalities is an important ...
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03/23/2020
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
With the advent of consumer grade depth sensors, low-cost volumetric cap...
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09/03/2019
Self-Supervised Deep Depth Denoising
Depth perception is considered an invaluable source of information for v...
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04/10/2017