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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 351360 of 9051 papers

TitleStatusHype
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Dataset GrowthCode1
Deep Color Transfer using Histogram AnalogyCode1
Deep Image Harmonization with Learnable AugmentationCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dan: Deep attention neural network for news recommendationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Active learning for medical image segmentation with stochastic batchesCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
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