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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 11011110 of 9051 papers

TitleStatusHype
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Dan: Deep attention neural network for news recommendationCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Dataset GrowthCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
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