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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 231240 of 9051 papers

TitleStatusHype
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language ModelsCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
SiLK -- Simple Learned KeypointsCode2
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
ReMoDiffuse: Retrieval-Augmented Motion Diffusion ModelCode2
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and ModelingCode2
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimizationCode2
Taming Diffusion Models for Audio-Driven Co-Speech Gesture GenerationCode2
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