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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 1681–1690 of 1854 papers

TitleStatusHype
Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps—0
Variational Learning with Disentanglement-PyTorchCode0
Disentanglement Challenge: From Regularization to Reconstruction—0
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement—0
Double cycle-consistent generative adversarial network for unsupervised conditional generation—0
Federated Adversarial Domain Adaptation—0
Self-supervised Deformation Modeling for Facial Expression Editing—0
Unsupervised Multi-Domain Multimodal Image-to-Image Translation with Explicit Domain-Constrained DisentanglementCode0
Learning Disentangled Representations for Recommendation—0
Weakly Supervised Disentanglement with GuaranteesCode0
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