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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 551560 of 9051 papers

TitleStatusHype
DiffWave: A Versatile Diffusion Model for Audio SynthesisCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Bayesian Adversarial Human Motion SynthesisCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
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