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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 26812690 of 9051 papers

TitleStatusHype
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology DiagnosisCode0
Diversity Augmented Conditional Generative Adversarial Network for Enhanced Multimodal Image-to-Image TranslationCode0
Few-shot Image Generation via Masked DiscriminationCode0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Analysis of the first Genetic Engineering Attribution ChallengeCode0
Few-shot Image Generation with Diffusion ModelsCode0
Federated Visual Classification with Real-World Data DistributionCode0
Federated Stain Normalization for Computational PathologyCode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
Federated Neural Topic ModelsCode0
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