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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

Showing 301310 of 1854 papers

TitleStatusHype
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion RetargetingCode1
TextStyleBrush: Transfer of Text Aesthetics from a Single ExampleCode1
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability PerspectiveCode1
Geometry-Consistent Neural Shape Representation with Implicit Displacement FieldsCode1
Self-Supervised Learning with Data Augmentations Provably Isolates Content from StyleCode1
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object RepresentationsCode1
Commutative Lie Group VAE for Disentanglement LearningCode1
Disentangled Face Attribute Editing via Instance-Aware Latent Space SearchCode1
Mask-Guided Discovery of Semantic Manifolds in Generative ModelsCode1
One Shot Face Swapping on MegapixelsCode1
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