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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 101110 of 9051 papers

TitleStatusHype
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty LearningCode2
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimizationCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerceCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
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