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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 1651–1675 of 1854 papers

TitleStatusHype
Identity-Disentangled Neural Deformation Model for Dynamic Meshes—0
IdentityDP: Differential Private Identification Protection for Face Images—0
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos—0
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction—0
iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre—0
iFADIT: Invertible Face Anonymization via Disentangled Identity Transform—0
IF-MDM: Implicit Face Motion Diffusion Model for High-Fidelity Realtime Talking Head Generation—0
Illumination-Adaptive Person Re-identification—0
Image Denoising via Style Disentanglement—0
Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation—0
Image Sentiment Transfer—0
Image Style Transfer and Content-Style Disentanglement—0
Impact of Disentanglement on Pruning Neural Networks—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
Improved Disentanglement through Aggregated Convolutional Feature Maps—0
Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps—0
Improved Neural Text Attribute Transfer with Non-parallel Data—0
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT—0
Improving Editability in Image Generation with Layer-wise Memory—0
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models—0
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows—0
Improving Style-Content Disentanglement in Image-to-Image Translation—0
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling—0
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble—0
Incremental Disentanglement for Environment-Aware Zero-Shot Text-to-Speech Synthesis—0
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