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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 1421–1430 of 1854 papers

TitleStatusHype
Property Controllable Variational Autoencoder via Invertible Mutual DependenceCode0
Learning to Disentangle Textual Representations and Attributes via Mutual Information—0
Multi-View Disentangled Representation—0
Unsupervised Disentanglement Learning by intervention—0
Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration—0
On the Role of Pre-training for Meta Few-Shot Learning—0
The role of Disentanglement in GeneralisationCode1
Addressing the Topological Defects of Disentanglement—0
Zero-shot Fairness with Invisible Demographics—0
Learning disentangled representations with the Wasserstein Autoencoder—0
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