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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 721–730 of 1854 papers

TitleStatusHype
Image-to-Image Translation with Disentangled Latent Vectors for Face Editing—0
FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio—0
Embodied Multimodal Multitask Learning—0
FaceCook: Face Generation Based on Linear Scaling Factors—0
Face Identity-Aware Disentanglement in StyleGAN—0
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement—0
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer—0
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement—0
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
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