SOTAVerified

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 1751–1800 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
Variational AutoEncoder For Regression: Application to Brain Aging AnalysisCode0
Gait Recognition via Disentangled Representation Learning—0
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence RepresentationsCode0
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
Challenging Common Assumptions in the Unsupervised Learning of Disentangled RepresentationsCode2
Unsupervised Domain-Specific Deblurring via Disentangled RepresentationsCode0
FAVAE: Sequence Disentanglement using Information Bottleneck PrincipleCode0
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image TranslationCode0
Relevance Factor VAE: Learning and Identifying Disentangled FactorsCode0
Embodied Multimodal Multitask Learning—0
Foreground-aware Image Inpainting—0
Learning a Generative Model of Cancer MetastasisCode0
Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation—0
Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation—0
A Style-Based Generator Architecture for Generative Adversarial NetworksCode2
Recent Advances in Autoencoder-Based Representation Learning—0
Disentangling Disentanglement in Variational AutoencodersCode0
A Spectral Regularizer for Unsupervised Disentanglement—0
Challenging Common Assumptions in the Unsupervised Learning of Disentangled RepresentationsCode1
Learning State Representations in Complex Systems with Multimodal Data—0
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and DiscoveryCode0
Style and Content Disentanglement in Generative Adversarial Networks—0
Multiple-Attribute Text Style TransferCode1
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT—0
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness—0
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