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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 901910 of 9051 papers

TitleStatusHype
Large-Vocabulary 3D Diffusion Model with TransformerCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Lattice CNNs for Matching Based Chinese Question AnsweringCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Learning Affordance Grounding from Exocentric ImagesCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Learning Diverse Risk Preferences in Population-based Self-playCode1
Learning from Synthetic Shadows for Shadow Detection and RemovalCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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