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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 37613770 of 9051 papers

TitleStatusHype
Exploring Attribute Variations in Style-based GANs using Diffusion Models0
Exploring and Exploiting Diversity for Image Segmentation0
Flow Score Distillation for Diverse Text-to-3D Generation0
Fluctuating growth rates link turnover and unevenness in species-rich communities0
Computational Eco-Systems for Handwritten Digits Recognition0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification0
A Simple Dual-decoder Model for Generating Response with Sentiment0
Focus Attention: Promoting Faithfulness and Diversity in Summarization0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning0
A guide through a family of phylogenetic dissimilarity measures among sites0
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