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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 41–50 of 1854 papers

TitleStatusHype
Compositional Transformers for Scene GenerationCode2
Compositional Transformers for Scene GenerationCode2
Generative Adversarial TransformersCode2
Learning an Animatable Detailed 3D Face Model from In-The-Wild ImagesCode2
Stylized Neural PaintingCode2
CausalVAE: Structured Causal Disentanglement in Variational AutoencoderCode2
Adversarial Latent AutoencodersCode2
Interpreting the Latent Space of GANs for Semantic Face EditingCode2
Challenging Common Assumptions in the Unsupervised Learning of Disentangled RepresentationsCode2
A Style-Based Generator Architecture for Generative Adversarial NetworksCode2
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