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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 1501–1510 of 1854 papers

TitleStatusHype
Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement—0
Learning disentangled representations with the Wasserstein Autoencoder—0
Sufficient and Disentangled Representation Learning—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Addressing the Topological Defects of Disentanglement—0
Learning to Disentangle Textual Representations and Attributes via Mutual Information—0
Information Theoretic Regularization for Learning Global Features by Sequential VAE—0
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