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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 10611070 of 9051 papers

TitleStatusHype
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
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