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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 4221–4230 of 9051 papers

TitleStatusHype
Application-Driven AI Paradigm for Person Counting in Various Scenarios—0
On the Efficacy of Generalization Error Prediction Scoring Functions—0
SAOR: Single-View Articulated Object Reconstruction—0
Is ChatGPT A Good Keyphrase Generator? A Preliminary StudyCode0
Improving Generalization with Domain Convex GameCode0
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
Controllable Inversion of Black-Box Face Recognition Models via Diffusion—0
TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo SupervisionCode1
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
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