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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 461–470 of 1854 papers

TitleStatusHype
Texture-GS: Disentangling the Geometry and Texture for 3D Gaussian Splatting Editing—0
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionCode1
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition—0
PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping—0
Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain TransferCode1
3D-aware Image Generation and Editing with Multi-modal Conditions—0
DEADiff: An Efficient Stylization Diffusion Model with Disentangled RepresentationsCode3
Disentangling shared and private latent factors in multimodal Variational AutoencodersCode0
MoST: Motion Style Transformer between Diverse Action ContentsCode1
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