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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 11211130 of 9051 papers

TitleStatusHype
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
Tackling the Generative Learning Trilemma with Denoising Diffusion GANsCode1
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed ClassificationCode1
Text Classification Models for Form Entity LinkingCode1
Learning Semantic-Aligned Feature Representation for Text-based Person SearchCode1
An Informative Tracking BenchmarkCode1
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary DetectionCode1
Adversarial Parametric Pose PriorCode1
Make It Move: Controllable Image-to-Video Generation with Text DescriptionsCode1
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