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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 1111–1120 of 1854 papers

TitleStatusHype
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
Learning Disentangled Label Representations for Multi-label Classification—0
CLIPascene: Scene Sketching with Different Types and Levels of Abstraction—0
Disentangled Generation with Information Bottleneck for Few-Shot Learning—0
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model—0
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task LearningCode0
Hand Avatar: Free-Pose Hand Animation and Rendering from Monocular Video—0
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation—0
Disentangled Feature Learning for Real-Time Neural Speech Coding—0
Multi-Directional Subspace Editing in Style-Space—0
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