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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 831840 of 9051 papers

TitleStatusHype
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent LearningCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
Class-Balancing Diffusion ModelsCode1
Generating images of rare concepts using pre-trained diffusion modelsCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic SegmentationCode1
Revisiting k-NN for Fine-tuning Pre-trained Language ModelsCode1
Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric ViewsCode1
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
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