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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 1821–1830 of 1854 papers

TitleStatusHype
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution—0
Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity—0
Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements—0
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion—0
Multi-modal Document Presentation Attack Detection With Forensics Trace Disentanglement—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization—0
Multi-Modal Latent Variables for Cross-Individual Primary Visual Cortex Modeling and Analysis—0
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