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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 131–140 of 1854 papers

TitleStatusHype
DifAttack: Query-Efficient Black-Box Attack via Disentangled Feature SpaceCode1
A Latent Transformer for Disentangled Face Editing in Images and VideosCode1
DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-IDCode1
DID-M3D: Decoupling Instance Depth for Monocular 3D Object DetectionCode1
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial DefenseCode1
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-DomainCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Beyond Prototypes: Semantic Anchor Regularization for Better Representation LearningCode1
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic ModelsCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
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