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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 11611170 of 9051 papers

TitleStatusHype
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Diversity-Aware Meta Visual PromptingCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
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