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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 11711180 of 9051 papers

TitleStatusHype
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection ApproachCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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