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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 1541–1550 of 1854 papers

TitleStatusHype
Learning to Decompose and Disentangle Representations for Video PredictionCode0
StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2Code0
Uncertainty Quantification in Stereo MatchingCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Replacing Language Model for Style TransferCode0
Learning Discrete and Continuous Factors of Data via Alternating DisentanglementCode0
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational AutoencodersCode0
Disentangling Past-Future Modeling in Sequential Recommendation via Dual NetworksCode0
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence RepresentationsCode0
Subspace Identification for Multi-Source Domain AdaptationCode0
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