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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 10211030 of 9051 papers

TitleStatusHype
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph CaptioningCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized ImagesCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Improving Diversity with Adversarially Learned Transformations for Domain GeneralizationCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Tackling covariate shift with node-based Bayesian neural networksCode1
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
Revealing the Dark Secrets of Masked Image ModelingCode1
Rethinking Fano's Inequality in Ensemble LearningCode1
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