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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 331340 of 9051 papers

TitleStatusHype
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Curriculum-guided Hindsight Experience ReplayCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
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