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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 15611570 of 9051 papers

TitleStatusHype
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
Soft Prompt Generation for Domain GeneralizationCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
Clotho: An Audio Captioning DatasetCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
InsetGAN for Full-Body Image GenerationCode1
Jointly Measuring Diversity and Quality in Text Generation ModelsCode1
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
Language-Grounded Indoor 3D Semantic Segmentation in the WildCode1
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