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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 1831–1840 of 1854 papers

TitleStatusHype
DGPose: Deep Generative Models for Human Body Analysis—0
Scalable Factorized Hierarchical Variational Autoencoder TrainingCode0
Structured Disentangled Representations—0
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data—0
Auto-Encoding Total Correlation Explanation—0
On the Latent Space of Wasserstein Auto-Encoders—0
Disentangled activations in deep networks—0
Preliminary theoretical troubleshooting in Variational Autoencoder—0
Improved Neural Text Attribute Transfer with Non-parallel Data—0
JADE: Joint Autoencoders for Dis-Entanglement—0
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