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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 4441–4450 of 9051 papers

TitleStatusHype
Testing High-dimensional Multinomials with Applications to Text Analysis—0
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape RecoveryCode0
Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning—0
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware SynthesisCode1
Text-Driven Generative Domain Adaptation with Spectral Consistency RegularizationCode0
Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild—0
Both Diverse and Realism Matter: Physical Attribute and Style Alignment for Rainy Image Generation—0
SiLK: Simple Learned Keypoints—0
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Self-Evolved Dynamic Expansion Model for Task-Free Continual LearningCode0
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