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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 16411650 of 9051 papers

TitleStatusHype
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct0
UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV DetectionCode1
Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Models0
Prim2Room: Layout-Controllable Room Mesh Generation from Primitives0
Seeing is Believing? Enhancing Vision-Language Navigation using Visual Perturbations0
LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow0
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identificationCode1
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Algorithmic Scenario Generation as Quality Diversity Optimization0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation0
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