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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 1501–1525 of 1854 papers

TitleStatusHype
Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement—0
Learning disentangled representations with the Wasserstein Autoencoder—0
Sufficient and Disentangled Representation Learning—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Addressing the Topological Defects of Disentanglement—0
Learning to Disentangle Textual Representations and Attributes via Mutual Information—0
Information Theoretic Regularization for Learning Global Features by Sequential VAE—0
Clearing the Path for Truly Semantic Representation Learning—0
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble—0
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations—0
An Assessment of GANs for Identity-related Applications—0
Multi-type Disentanglement without Adversarial Training—0
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement—0
Odd-One-Out Representation LearningCode0
DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning—0
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend ForecastingCode0
Physics-Guided Spoof Trace Disentanglement for Generic Face Anti-Spoofing—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy PreservationCode0
Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements—0
MakeupBag: Disentangling Makeup Extraction and Application—0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
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