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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 371–380 of 1854 papers

TitleStatusHype
ShapeFlow: Learnable Deformations Among 3D ShapesCode1
On Disentangled Representations Learned From Correlated DataCode1
Deep Dimension Reduction for Supervised Representation LearningCode1
DisCont: Self-Supervised Visual Attribute Disentanglement using Context VectorsCode1
VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architectureCode1
Evaluating the Disentanglement of Deep Generative Models through Manifold TopologyCode1
Arbitrary Style Transfer via Multi-Adaptation NetworkCode1
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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