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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 10611070 of 9051 papers

TitleStatusHype
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Bias Loss for Mobile Neural NetworksCode1
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Fork or Fail: Cycle-Consistent Training with Many-to-One MappingsCode1
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
A Multimodal In-Context Tuning Approach for E-Commerce Product Description GenerationCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
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