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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 56015610 of 9051 papers

TitleStatusHype
Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity0
Text-Only Training for Visual Storytelling0
Text Semantics to Flexible Design: A Residential Layout Generation Method Based on Stable Diffusion Model0
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors0
Text to Image Generation: Leaving no Language Behind0
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork0
Text-to-Image Synthesis: A Decade Survey0
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression0
Se^2: Sequential Example Selection for In-Context Learning0
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models0
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