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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 3541–3550 of 9051 papers

TitleStatusHype
FaceCook: Face Generation Based on Linear Scaling Factors—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Diversified Late Acceptance Search—0
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis—0
FaceFormer: Scale-aware Blind Face Restoration with Transformers—0
ConfigTron: Tackling network diversity with heterogeneous configurations—0
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation—0
Face-to-Music Translation Using a Distance-Preserving Generative Adversarial Network with an Auxiliary Discriminator—0
Facets of Fairness in Search and Recommendation—0
Bootstrapping NLP tools across low-resourced African languages: an overview and prospects—0
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