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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 12911300 of 9051 papers

TitleStatusHype
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
GS-Blur: A 3D Scene-Based Dataset for Realistic Image DeblurringCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Data Curation Alone Can Stabilize In-context LearningCode1
Harvesting Event Schemas from Large Language ModelsCode1
Heterogeneous Multi-task Learning with Expert DiversityCode1
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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