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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 1031–1040 of 1854 papers

TitleStatusHype
StyleGenes: Discrete and Efficient Latent Distributions for GANs—0
Visual Referential Games Further the Emergence of Disentangled Representations—0
NaviNeRF: NeRF-based 3D Representation Disentanglement by Latent Semantic NavigationCode0
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art AnalysisCode0
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning—0
Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation—0
Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance VocabularyCode0
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer—0
Leveraging Neural Representations for Audio Manipulation—0
TC-VAE: Uncovering Out-of-Distribution Data Generative Factors—0
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