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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 1251–1300 of 1854 papers

TitleStatusHype
Towards Controllable and Photorealistic Region-wise Image Manipulation—0
Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose TransferCode1
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image GenerationCode1
Vector-Decomposed Disentanglement for Domain-Invariant Object DetectionCode1
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future DirectionsCode0
No-Reference Image Quality Assessment by Hallucinating Pristine FeaturesCode1
A Unified Model for Zero-shot Music Source Separation, Transcription and SynthesisCode1
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer—0
Is Disentanglement enough? On Latent Representations for Controllable Music GenerationCode1
Fair Representation Learning using Interpolation Enabled Disentanglement—0
Physics-informed Guided Disentanglement in Generative NetworksCode1
Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss—0
AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling TrainingCode0
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICACode1
β-Annealed Variational Autoencoder for glitches—0
Discriminator-Free Generative Adversarial AttackCode1
A Systematical Solution for Face De-identification—0
Adversarial Continual Learning for Multi-Domain Hippocampal SegmentationCode1
StyleFusion: A Generative Model for Disentangling Spatial SegmentsCode1
Retrieve in Style: Unsupervised Facial Feature Transfer and RetrievalCode1
Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder—0
Semantic and Geometric Unfolding of StyleGAN Latent Space—0
Disentangle Your Dense Object DetectorCode1
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation—0
Conditional Identity Disentanglement for Differential Face Morph Detection—0
Semantic StyleGAN—0
Exploring the Latent Space of Autoencoders with Interventional AssaysCode0
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the WildCode1
Intent Disentanglement and Feature Self-supervision for Novel Recommendation—0
Unsupervised Skill Discovery with Bottleneck Option LearningCode1
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image SegmentationCode1
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual LearningCode0
Volume Rendering of Neural Implicit SurfacesCode1
A Latent Transformer for Disentangled Face Editing in Images and VideosCode1
Enhanced Separable Disentanglement for Unsupervised Domain Adaptation—0
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement—0
Information Bottleneck Disentanglement for Identity SwappingCode1
L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute EditingCode1
A Max-Min Entropy Framework for Reinforcement LearningCode1
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice ConversionCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion RetargetingCode1
Disentangling Online Chats with DAG-Structured LSTMs—0
TextStyleBrush: Transfer of Text Aesthetics from a Single ExampleCode1
Graph Domain Adaptation: A Generative View—0
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders—0
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability PerspectiveCode1
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsCode0
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