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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 731740 of 9051 papers

TitleStatusHype
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change DetectionCode1
Probabilistic Precision and Recall Towards Reliable Evaluation of Generative ModelsCode1
Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolutionCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
MolGrapher: Graph-based Visual Recognition of Chemical StructuresCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
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