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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

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Showing 3671–3680 of 9051 papers

TitleStatusHype
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Few-shot Image Generation Using Discrete Content Representation—0
Diverse Single Image Generation with Controllable Global Structure—0
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface—0
Controllable and Diverse Text Generation in E-commerce—0
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning—0
Few-shot Image Generation via Style Adaptation and Content Preservation—0
Few-shot Image Generation with Elastic Weight Consolidation—0
An Improved Dung Beetle Optimizer for Random Forest Optimization—0
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