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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 971980 of 9051 papers

TitleStatusHype
GenDexGrasp: Generalizable Dexterous GraspingCode1
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
Parea: multi-view ensemble clustering for cancer subtype discoveryCode1
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training DynamicsCode1
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputCode1
Learning Distinct and Representative Styles for Image CaptioningCode1
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