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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 301310 of 9051 papers

TitleStatusHype
GenSim: Generating Robotic Simulation Tasks via Large Language ModelsCode2
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Adapting Precomputed Features for Efficient Graph CondensationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
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