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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 37713780 of 9051 papers

TitleStatusHype
Exploring Attribute Variations in Style-based GANs using Diffusion Models0
Exploring and Exploiting Diversity for Image Segmentation0
Computational Eco-Systems for Handwritten Digits Recognition0
Font Identification in Historical Documents Using Active Learning0
A Simple Dual-decoder Model for Generating Response with Sentiment0
A guide through a family of phylogenetic dissimilarity measures among sites0
Active Learning for Deep Visual Tracking0
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder0
Navigating Text-to-Image Generative Bias across Indic Languages0
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