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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 781790 of 9051 papers

TitleStatusHype
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
Understanding Deep Generative Models with Generalized Empirical LikelihoodsCode1
Taming Diffusion Models for Music-driven Conducting Motion GenerationCode1
Domain-specific ChatBots for Science using EmbeddingsCode1
Text Promptable Surgical Instrument Segmentation with Vision-Language ModelsCode1
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative ModelsCode1
Feature Fusion from Head to Tail for Long-Tailed Visual RecognitionCode1
Towards Diverse and Effective Question-Answer Pair Generation from Children StorybooksCode1
Stochastic Multi-Person 3D Motion ForecastingCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
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