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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 331–340 of 1854 papers

TitleStatusHype
Adaptive Nonlinear Latent Transformation for Conditional Face EditingCode1
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual InformationCode1
Identifiability Guarantees for Causal Disentanglement from Soft InterventionsCode1
Image Disentanglement Autoencoder for Steganography Without EmbeddingCode1
DisCont: Self-Supervised Visual Attribute Disentanglement using Context VectorsCode1
A Max-Min Entropy Framework for Reinforcement LearningCode1
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANsCode1
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive RegularizersCode1
A robust estimator of mutual information for deep learning interpretabilityCode1
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICACode1
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