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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 791–800 of 1854 papers

TitleStatusHype
EDTalk: Efficient Disentanglement for Emotional Talking Head Synthesis—0
Frequency Disentangled Features in Neural Image Compression—0
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data—0
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training—0
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis—0
Gait Recognition via Disentangled Representation Learning—0
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features—0
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer—0
Counterfactual Explanation for Regression via Disentanglement in Latent Space—0
ED^4: Explicit Data-level Debiasing for Deepfake Detection—0
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