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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 471–480 of 1854 papers

TitleStatusHype
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
Towards a Unified Representation Evaluation Framework Beyond Downstream TasksCode0
Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection—0
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksCode0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution—0
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation—0
Causal Intervention Framework for Variational Auto Encoder Mechanistic Interpretability—0
GenSync: A Generalized Talking Head Framework for Audio-driven Multi-Subject Lip-Sync using 3D Gaussian Splatting—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Improving Editability in Image Generation with Layer-wise Memory—0
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