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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 45314540 of 9051 papers

TitleStatusHype
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review0
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model0
DATTA: Towards Diversity Adaptive Test-Time Adaptation in Dynamic Wild World0
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild0
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition0
DCAN: Diversified News Recommendation with Coverage-Attentive Networks0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting0
DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning0
DDS: A new device-degraded speech dataset for speech enhancement0
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