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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 1461–1470 of 1854 papers

TitleStatusHype
Unsupervised Geometric Disentanglement via CFAN-VAE—0
U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction—0
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data—0
CoDeGAN: Contrastive Disentanglement for Generative Adversarial NetworkCode0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation—0
IdentityDP: Differential Private Identification Protection for Face Images—0
Learning disentangled representations via product manifold projection—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
FaceController: Controllable Attribute Editing for Face in the Wild—0
ShaRF: Shape-conditioned Radiance Fields from a Single View—0
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