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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 791800 of 9051 papers

TitleStatusHype
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
Contrastive Syn-to-Real GeneralizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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