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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 1751–1775 of 1854 papers

TitleStatusHype
Disentangling Style and Content in Anime Illustrations—0
Learning Discrete and Continuous Factors of Data via Alternating DisentanglementCode0
Additive Adversarial Learning for Unbiased AuthenticationCode0
Disentangling Content and Style via Unsupervised Geometry DistillationCode0
Illumination-Adaptive Person Re-identification—0
Unified Adversarial Invariance—0
Disentangling Factors of Variation Using Few Labels—0
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice ConversionCode1
Critical Learning Periods in Deep Networks—0
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian SupervisionCode0
Neural TTS Stylization with Adversarial and Collaborative Games—0
Cross-Task Knowledge Transfer for Visually-Grounded Navigation—0
INFORMATION MAXIMIZATION AUTO-ENCODING—0
ISA-VAE: Independent Subspace Analysis with Variational Autoencoders—0
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE—0
Multiple-Attribute Text Rewriting—0
IB-GAN: Disentangled Representation Learning with Information Bottleneck GANCode0
Non-Synergistic Variational Autoencoders—0
Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised LearningCode0
Out of the Box: A combined approach for handling occlusion in Human Pose Estimation—0
Catch Me If You Can—0
Inspecting and Interacting with Meaningful Music Representations using VAE—0
Learning Interpretable Disentangled Representations using Adversarial VAEs—0
Disentangling Pose from Appearance in Monochrome Hand Images—0
Towards Photographic Image Manipulation with Balanced Growing of Generative AutoencodersCode0
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