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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 261270 of 9051 papers

TitleStatusHype
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
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