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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 49414950 of 9051 papers

TitleStatusHype
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition0
Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification0
Generalized Probabilistic U-Net for medical image segementationCode1
Static and Dynamic Concepts for Self-supervised Video Representation LearningCode1
Classifier-Free Diffusion GuidanceCode2
Bugs as Features (Part I): Concepts and Foundations for the Compositional Data Analysis of the Microbiome-Gut-Brain Axis0
Representational Ethical Model Calibration0
Domain-invariant Feature Exploration for Domain Generalization0
CelebV-HQ: A Large-Scale Video Facial Attributes DatasetCode2
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification0
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