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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 841850 of 9051 papers

TitleStatusHype
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
HUMOS: Human Motion Model Conditioned on Body ShapeCode1
AI-generated text boundary detection with RoFTCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDCode1
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial NetworkCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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