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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 41014110 of 9051 papers

TitleStatusHype
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires0
Concept-Monitor: Understanding DNN training through individual neurons0
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation0
Learning Trajectories are Generalization Indicators0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
GlyphDiffusion: Text Generation as Image Generation0
Disagreement amongst counterfactual explanations: How transparency can be deceptive0
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