Beijing, China

Ruifei He

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignees: Beijing Youzhuju Network Technology Co., Ltd., Lemon Inc.. Active years: 2026.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Ruifei He: Innovator in Model Training Based on Synthetic Data

Introduction

Ruifei He is a notable inventor based in Beijing, China. He has made significant contributions to the field of model training, particularly through his innovative approach to using synthetic data. His work aims to enhance the performance of image classification models, especially in data-scarce environments.

Latest Patents

Ruifei He holds 1 patent for his invention titled "Model training based on synthetic data." This patent describes a method where synthetic images are generated using text prompts in a text-to-image generation model. The associated training labels for these synthetic images are also created based on the text prompts. The target model, designed for image classification tasks, is trained using these synthetic images and labels. This approach allows for the automatic generation of a large scale of synthetic images, which can significantly improve model performance in scenarios where training data is limited.

Career Highlights

Throughout his career, Ruifei He has worked with several companies, including Lemon Inc. and Beijing Youzhuju Network Technology Co., Ltd. His experience in these organizations has contributed to his expertise in the field of artificial intelligence and model training.

Collaborations

Ruifei He has collaborated with notable colleagues such as Chuhui Xue and Song Bai. Their joint efforts have likely fostered advancements in their respective projects and research initiatives.

Conclusion

Ruifei He is a prominent figure in the realm of synthetic data and model training. His innovative patent and career achievements reflect his dedication to improving technology in image classification.

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