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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 261270 of 9051 papers

TitleStatusHype
Diffusion Bridge Implicit ModelsCode2
AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data GenerationCode2
Deep Rectangling for Image Stitching: A Learning BaselineCode2
Delta Decompression for MoE-based LLMs CompressionCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
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