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Disentanglement

This is an approach to solve a diverse set of tasks in a data efficient manner by disentangling (or isolating ) the underlying structure of the main problem into disjoint parts of its representations. This disentanglement can be done by focussing on the "transformation" properties of the world(main problem)

Papers

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Showing 1571–1580 of 1854 papers

TitleStatusHype
Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation—0
Semi-supervised Pathology Segmentation with Disentangled RepresentationsCode0
Surrogate Model For Field Optimization Using Beta-VAE Based Regression—0
iCaps: An Interpretable Classifier via Disentangled Capsule Networks—0
Face Anti-Spoofing Via Disentangled Representation Learning—0
Linear Disentangled Representations and Unsupervised Action Estimation—0
Null-sampling for Interpretable and Fair RepresentationsCode0
Metric Learning vs Classification for Disentangled Music Representation Learning—0
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization—0
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-IdentificationCode0
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