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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 861870 of 9051 papers

TitleStatusHype
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision ModelsCode1
Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog ArchitecturesCode1
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identificationCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
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