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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 351–360 of 9051 papers

TitleStatusHype
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Deep Color Transfer using Histogram AnalogyCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Active learning for medical image segmentation with stochastic batchesCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
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