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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 371380 of 9051 papers

TitleStatusHype
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
Dataset Factorization for CondensationCode1
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
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