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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 32113220 of 9051 papers

TitleStatusHype
Accelerated Image-Aware Generative Diffusion Modeling0
Cultural Diversity and Its Impact on Governance0
Semantic and Expressive Variation in Image Captions Across Languages0
A Unified Statistical Model for Atmospheric Turbulence-Induced Fading in Orbital Angular Momentum Multiplexed FSO Systems0
Cultivating DNN Diversity for Large Scale Video Labelling0
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network0
A Unified Multi-Faceted Video Summarization System0
All You Need Is Sex for Diversity0
Cuid: A new study of perceived image quality and its subjective assessment0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution0
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