Beijing Zitiao Network Technology Co., Ltd. (20240320966). METHOD AND DEVICE FOR DATA PROCESSING simplified abstract
Contents
METHOD AND DEVICE FOR DATA PROCESSING
Organization Name
Beijing Zitiao Network Technology Co., Ltd.
Inventor(s)
METHOD AND DEVICE FOR DATA PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320966 titled 'METHOD AND DEVICE FOR DATA PROCESSING
Simplified Explanation:
This patent application describes a method and device for processing data using a generative adversarial network obtained through model distillation. The network consists of a first generator, a second generator, and a discriminator. The method involves processing an image with the first generator to obtain a processed image.
- Model distillation process involving training the first and second generators alternately.
- First generator has a smaller model scale compared to the second generator.
- Processing an image with the first generator to obtain a processed image.
Key Features and Innovation:
- Utilization of a generative adversarial network obtained through model distillation.
- Alternating training of the first and second generators.
- Different model scales for the first and second generators.
Potential Applications:
- Image processing and enhancement.
- Data generation for various applications.
- Artificial intelligence and machine learning tasks.
Problems Solved:
- Efficient data processing with the use of model distillation.
- Improved image processing capabilities.
- Enhanced performance of generative adversarial networks.
Benefits:
- Higher quality processed images.
- Faster data processing.
- Enhanced performance of the network.
Commercial Applications: Generative adversarial networks obtained through model distillation can be used in industries such as:
- Image editing and enhancement software development.
- Artificial intelligence research and development.
- Data generation for various applications.
Questions about the Technology: 1. How does the model distillation process improve the performance of the generative adversarial network? 2. What are the potential limitations of using a smaller model scale for the first generator in the network?
Original Abstract Submitted
a method and a device for data processing. the method is adapted to a generative adversarial network obtained by model distillation, and the generative adversarial network includes a first generator, a second generator and a discriminator. the model distillation is a process of alternately training the first generator and the second generator. a model scale of the first generator is smaller than a model scale of the second generator. the method includes: obtaining an image to be processed; and processing the image with a first generator to obtain a processed image.
