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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 1661–1670 of 1854 papers

TitleStatusHype
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models—0
Towards Visually Explaining Variational AutoencodersCode0
All-In-One: Facial Expression Transfer, Editing and Recognition Using A Single Network—0
Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps—0
Disentanglement Challenge: From Regularization to Reconstruction—0
PVAE: Learning Disentangled Representations with Intrinsic Dimension via Approximated L0 RegularizationCode0
Variational Learning with Disentanglement-PyTorchCode0
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
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