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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 561570 of 9051 papers

TitleStatusHype
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
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