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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

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Showing 5426–5450 of 9051 papers

TitleStatusHype
VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and AdaptationCode1
Diversity in deep generative models and generative AICode0
Deep Single Image Deraining using An Asymetric Cycle Generative and Adversarial Framework—0
Study of Feature Importance for Quantum Machine Learning Models—0
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)—0
Towards Simple and Accurate Human Pose Estimation with Stair Network—0
On the ideas of the origin of eukaryotes: a critical review—0
Point Cloud Generation with Continuous Conditioning—0
Realistic Blur Synthesis for Learning Image DeblurringCode1
3D-Aware Indoor Scene Synthesis with Depth Priors—0
Total consensus under high reproductive-variance conditions—0
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity—0
Deep Ensembles Work, But Are They Necessary?Code0
Distribution augmentation for low-resource expressive text-to-speech—0
A Contrastive Framework for Neural Text GenerationCode2
PQuAD: A Persian Question Answering Dataset—0
Privacy protection based on mask template—0
Data standardization for robust lip sync—0
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style TransformationCode1
Multi-level Latent Space Structuring for Generative Control—0
Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation—0
Fast Rates in Pool-Based Batch Active Learning—0
Consistency and Diversity induced Human Motion Segmentation—0
InPars: Data Augmentation for Information Retrieval using Large Language ModelsCode2
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning—0
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