SOTAVerified

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 32713280 of 9051 papers

TitleStatusHype
Reconstructing Objects in-the-wild for Realistic Sensor Simulation0
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data0
MixTEA: Semi-supervised Entity Alignment with Mixture TeachingCode0
Assessing Distractors in Multiple-Choice Tests0
CLearViD: Curriculum Learning for Video Description0
RankAug: Augmented data ranking for text classification0
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation0
Bias and Diversity in Synthetic-based Face Recognition0
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