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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 1276–1300 of 1854 papers

TitleStatusHype
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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