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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 971–980 of 1854 papers

TitleStatusHype
Disentangling Identity and Pose for Facial Expression Recognition—0
Disentangled Speaker Representation Learning via Mutual Information Minimization—0
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity DisentanglementCode0
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement PerspectiveCode1
Disentangled Representation Learning Using (β-)VAE and GAN—0
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual InformationCode1
Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment—0
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution—0
Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding—0
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift—0
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