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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 11311140 of 9051 papers

TitleStatusHype
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets0
Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management0
Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding0
Text-to-Image Synthesis: A Decade Survey0
PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation0
VisualLens: Personalization through Visual History0
Image Generation Diversity Issues and How to Tame ThemCode1
Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh RecoveryCode0
Towards Foundation Models for Critical Care Time Series0
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