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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 731740 of 9051 papers

TitleStatusHype
Adversarial Parametric Pose PriorCode1
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and DetectionCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Any-Play: An Intrinsic Augmentation for Zero-Shot CoordinationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
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