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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 10611070 of 9051 papers

TitleStatusHype
Hierarchical Quality-Diversity for Online Damage RecoveryCode1
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise AffinityCode1
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New BaselineCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Leverage Your Local and Global Representations: A New Self-Supervised Learning StrategyCode1
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword SpottingCode1
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry ConstraintsCode1
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