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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 951975 of 9051 papers

TitleStatusHype
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
Contrastive Syn-to-Real GeneralizationCode1
Attributed Graph Clustering with Dual Redundancy ReductionCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample PredictionCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich ReasoningCode1
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM ImagesCode1
MGF: Mixed Gaussian Flow for Diverse Trajectory PredictionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment DatabaseCode1
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
Contextual Diversity for Active LearningCode1
Mitigating Open-Vocabulary Caption HallucinationsCode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
Modeling Caption Diversity in Contrastive Vision-Language PretrainingCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
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