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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
Nested Scale Editing for Conditional Image Synthesis—0
Nested Scale-Editing for Conditional Image Synthesis—0
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features—0
Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation—0
Arbitrary Style Transfer via Multi-Adaptation NetworkCode1
Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE—0
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation—0
Style Normalization and Restitution for Generalizable Person Re-identificationCode1
Robust Training of Vector Quantized Bottleneck ModelsCode1
Face Identity Disentanglement via Latent Space MappingCode1
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