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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 40014010 of 9051 papers

TitleStatusHype
Generative AI for Physical Layer Communications: A Survey0
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era0
A Co-analysis Framework for Exploring Multivariate Scientific Data0
Generative artificial intelligence enhances creativity but reduces the diversity of novel content0
Generative Augmented Flow Networks0
3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing0
Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation0
Diverse Image Annotation0
Generative Dataset Distillation using Min-Max Diffusion Model0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing0
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