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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 121–130 of 1854 papers

TitleStatusHype
DEVIAS: Learning Disentangled Video Representations of Action and SceneCode1
When StyleGAN Meets Stable Diffusion: a W_+ Adapter for Personalized Image GenerationCode1
Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and CaptionsCode1
The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In VisibilityCode1
LFSRDiff: Light Field Image Super-Resolution via Diffusion ModelsCode1
DreamCreature: Crafting Photorealistic Virtual Creatures from ImaginationCode1
Parameter Exchange for Robust Dynamic Domain GeneralizationCode1
Multi-View Causal Representation Learning with Partial ObservabilityCode1
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational AutoencoderCode1
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic DataCode1
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