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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 561570 of 9051 papers

TitleStatusHype
Cross-Covariate Gait Recognition: A BenchmarkCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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