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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 11411150 of 9051 papers

TitleStatusHype
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
Adaptively Sparse TransformersCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Clotho: An Audio Captioning DatasetCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Group-wise Inhibition based Feature Regularization for Robust ClassificationCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Fractal Autoencoders for Feature SelectionCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
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