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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 46314640 of 9051 papers

TitleStatusHype
Pushing the limits of self-supervised speaker verification using regularized distillation framework0
Synchronization of Diverse Agents via Phase Analysis0
Unsupervised vocal dereverberation with diffusion-based generative models0
Uncertainty Quantification for Atlas-Level Cell Type Transfer0
Few-shot Image Generation with Diffusion ModelsCode0
Using Set Covering to Generate Databases for Holistic SteganalysisCode0
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow0
A review of TinyML0
SizeGAN: Improving Size Representation in Clothing Catalogs0
SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-SpoofingCode1
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