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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 1521–1530 of 1854 papers

TitleStatusHype
Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information—0
Colorization of Depth Map via DisentanglementCode0
Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification—0
Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature ModellingCode1
dMelodies: A Music Dataset for Disentanglement LearningCode1
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation—0
Learning Disentangled Representations with Latent Variation PredictabilityCode1
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement—0
Unsupervised Shape and Pose Disentanglement for 3D MeshesCode1
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