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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 12211230 of 9051 papers

TitleStatusHype
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Contrastive Syn-to-Real GeneralizationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
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