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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 4521–4530 of 9051 papers

TitleStatusHype
Frugal Reinforcement-based Active Learning—0
Effective Dynamics of Generative Adversarial Networks—0
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Learning Video Representations from Large Language ModelsCode2
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing—0
Learning to Select Prototypical Parts for Interpretable Sequential Data ModelingCode0
Curiosity creates Diversity in Policy Search—0
M3ST: Mix at Three Levels for Speech Translation—0
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