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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 10311040 of 9051 papers

TitleStatusHype
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Syn-to-Real GeneralizationCode1
Contextual Diversity for Active LearningCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
DebateQA: Evaluating Question Answering on Debatable KnowledgeCode1
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