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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 4641–4650 of 9051 papers

TitleStatusHype
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AICode0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning—0
A General Purpose Neural Architecture for Geospatial Systems—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Dataset Factorization for CondensationCode1
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