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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 1571–1580 of 1854 papers

TitleStatusHype
Federated Adversarial Domain Adaptation—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Federated Generalized Face Presentation Attack Detection—0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
FedPD: Federated Open Set Recognition with Parameter Disentanglement—0
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—0
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data—0
Findings on Conversation Disentanglement—0
Fine-grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement—0
Flexibly Fair Representation Learning by Disentanglement—0
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