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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 1791–1800 of 1854 papers

TitleStatusHype
Make Your Actor Talk: Generalizable and High-Fidelity Lip Sync with Motion and Appearance Disentanglement—0
Manifold Learning and Alignment with Generative Adversarial Networks—0
Manipulating Medical Image Translation with Manifold Disentanglement—0
Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder—0
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets—0
Measuring Orthogonality in Representations of Generative Models—0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation—0
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning—0
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