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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

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Showing 2421–2430 of 9051 papers

TitleStatusHype
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionCode0
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANsCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text GenerationCode0
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT ImagesCode0
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in HanabiCode0
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