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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 21912200 of 9051 papers

TitleStatusHype
Hi5: 2D Hand Pose Estimation with Zero Human Annotation0
Understanding the Limitations of Diffusion Concept Algebra Through Food0
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language ModelsCode2
Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis0
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection0
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding PerspectiveCode1
Interactive Image Selection and Training for Brain Tumor Segmentation Network0
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN0
Highway Value Iteration Networks0
OpenDataLab: Empowering General Artificial Intelligence with Open Datasets0
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