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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 131140 of 9051 papers

TitleStatusHype
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Conditional Image Synthesis with Diffusion Models: A SurveyCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
HSIGene: A Foundation Model For Hyperspectral Image GenerationCode2
Vista3D: Unravel the 3D Darkside of a Single ImageCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Segment Any Mesh: Zero-shot Mesh Part Segmentation via Lifting Segment Anything 2 to 3DCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
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