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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 1681–1690 of 1854 papers

TitleStatusHype
INFORMATION MAXIMIZATION AUTO-ENCODING—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
Information Theoretic Regularization for Learning Global Features by Sequential VAE—0
InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer—0
InLINE: Inner-Layer Information Exchange for Multi-task Learning on Heterogeneous Graphs—0
Inspecting and Interacting with Meaningful Music Representations using VAE—0
Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement—0
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization—0
Intent Disentanglement and Feature Self-supervision for Novel Recommendation—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
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