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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
FaceController: Controllable Attribute Editing for Face in the Wild—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
FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention—0
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers—0
Factorized RVQ-GAN For Disentangled Speech Tokenization—0
Factorized Visual Tokenization and Generation—0
FADES: Fair Disentanglement with Sensitive Relevance—0
Fair-CDA: Continuous and Directional Augmentation for Group Fairness—0
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