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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 14711480 of 9051 papers

TitleStatusHype
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Implicit Neural Representations for Variable Length Human Motion GenerationCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
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