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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 27912800 of 9051 papers

TitleStatusHype
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory0
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder0
Diversity Promoting Online Sampling for Streaming Video Summarization0
Diversity regularization in deep ensembles0
Diversity Regularized Adversarial Learning0
Diversity Regularized Interests Modeling for Recommender Systems0
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification0
Diversity-Rewarded CFG Distillation0
Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data0
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