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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 10411050 of 9051 papers

TitleStatusHype
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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