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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 161170 of 9051 papers

TitleStatusHype
Diffusion Bridge Implicit ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Diverse Preference OptimizationCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
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