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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 1161–1170 of 1854 papers

TitleStatusHype
Image Style Transfer and Content-Style Disentanglement—0
Optimizing Latent Space Directions For GAN-based Local Image EditingCode1
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and FacesCode1
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GANCode1
Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance—0
Compositional Transformers for Scene GenerationCode2
Learning Disentangled Representations in Natural Language Definitions with Semantic Role Labeling Supervision—0
Interpretability on clinical analysis from Pattern Disentanglement Insight—0
Structural Characterization for Dialogue Disentanglement—0
Disentangled Sequence to Sequence Learning for Compositional Generalization—0
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