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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 1601–1625 of 1854 papers

TitleStatusHype
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications—0
Kinematic-Structure-Preserved Representation for Unsupervised 3D Human Pose Estimation—0
Feature Alignment and Restoration for Domain Generalization and Adaptation—0
Image Sentiment Transfer—0
Learning from Demonstration with Weakly Supervised Disentanglement—0
Disentanglement for Discriminative Visual Recognition—0
Structure by Architecture: Structured Representations without Regularization—0
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement—0
Disentangled Representation Learning and Generation with Manifold Optimization—0
An Improved Semi-Supervised VAE for Learning Disentangled Representations—0
Longitudinal Self-Supervised Learning—0
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning—0
Interpretable Deep Graph Generation with Node-Edge Co-DisentanglementCode0
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
Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE—0
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation—0
Variance Constrained Autoencoding—0
Disentangling Factors of Variations Using Few Labels—0
One-Shot Identity-Preserving Portrait Reenactment—0
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation—0
Pseudo-healthy synthesis with pathology disentanglement and adversarial learningCode0
Compositionality and Generalization in Emergent Languages—0
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