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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 891900 of 9051 papers

TitleStatusHype
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Syn-to-Real GeneralizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Large Scale Image Completion via Co-Modulated Generative Adversarial NetworksCode1
Large-scale Unsupervised Semantic SegmentationCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
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