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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 91–100 of 1854 papers

TitleStatusHype
Purify Unlearnable Examples via Rate-Constrained Variational AutoencodersCode1
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic ModelsCode1
3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency PriorCode1
Cooperative Sentiment Agents for Multimodal Sentiment AnalysisCode1
Disentangling ID and Modality Effects for Session-based RecommendationCode1
Tripod: Three Complementary Inductive Biases for Disentangled Representation LearningCode1
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self AugmentationCode1
Non-negative Contrastive LearningCode1
SF(DA)^2: Source-free Domain Adaptation Through the Lens of Data AugmentationCode1
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionCode1
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