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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 761–770 of 1854 papers

TitleStatusHype
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement—0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
AvatarReX: Real-time Expressive Full-body Avatars—0
Federated Generalized Face Presentation Attack Detection—0
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder—0
FedPD: Federated Open Set Recognition with Parameter Disentanglement—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax—0
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