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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 1421–1430 of 1854 papers

TitleStatusHype
LLM-driven Multimodal and Multi-Identity Listening Head Generation—0
LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models—0
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction—0
LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec—0
Longitudinal Self-Supervised Learning—0
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
Make a Face: Towards Arbitrary High Fidelity Face Manipulation—0
MakeupBag: Disentangling Makeup Extraction and Application—0
Make Your Actor Talk: Generalizable and High-Fidelity Lip Sync with Motion and Appearance Disentanglement—0
Manifold Learning and Alignment with Generative Adversarial Networks—0
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