Generative Adversarial Networks: Intuitive Principles and Simple Applications

Generative Adversarial Networks: Intuitive Principles and Simple Applications

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Generative Adversarial Networks: Intuitive Principles and Simple Applications
On September 17, Professor Fan Lei from Capital Normal University shared a presentation titled “Generative Adversarial Networks – Intuitive Principles and Simple Applications” at the Yuanzhuo Academy. He introduced the basic concepts of Generative Adversarial Networks (GANs) and constructed a simple GAN model to help everyone understand the memory of familiar problems.
Professor Fan introduced the concept and development process of GANs, making comparisons to previously learned knowledge and facilitating knowledge transfer. GANs are an important component of the new generation of artificial intelligence, represented by deep learning, which has rapidly emerged. GANs introduce ideas that differ from traditional neural networks and convolutional neural networks, transforming the traditional unidirectional propagation into an interaction between data.
During his explanation, Professor Fan pointed out that the basic idea of GANs involves two neural networks, one being the generator and the other the discriminator, designed as competitive opponents.The standard GAN training process mainly includes three steps:training the discriminator with real training datasets, training the discriminator with fabricated generated data, and training the generator to produce data while “guiding” the discriminator to classify it as real data.Professor Fan illustrated the GAN training establishment process with a vivid example, detailing the specifics and principles of each step.
Generative Adversarial Networks: Intuitive Principles and Simple Applications
Visual Example of the Three Steps of GAN Training
After detailing each step of GAN training, Professor Fan provided the architecture of the GAN model and made comparisons with neural network model architecture, reinforcing and deepening everyone’s memory.
Addressing potential issues in GAN training, Professor Fan introduced three improvement methods for GAN training and explained the applicable scenarios and pros and cons of each method.
Generative Adversarial Networks: Intuitive Principles and Simple Applications
Code Screenshot of the GAN Training Modeling Process
For this session’s video replay and related materials, please visit the Yuanzhuo Project official website https://yuanzhuo.bnu.edu.cn/my/course/153 orclick the link at the end to read the original text to obtain.
Previous activity video replays and related materials have been uploaded, please visit the Yuanzhuo Project official website https://yuanzhuo.bnu.edu.cn to obtain.
Teachers are welcome to continue to communicate and learn from each other on the Yuanzhuo Project platform for mutual progress.

Since January 2022, the Yuanzhuo Project has been continuously carrying out community activities (every Saturday from 10:00 to 11:50 on Tencent Meeting: 677-4412-3805), encouraging young people to use artificial intelligence to create and innovate algorithms to solve real problems, building a collaborative mechanism among universities, primary and secondary schools, and technology enterprises, collecting youth artificial intelligence projects for nurturing, and providing comprehensive support such as algorithms, computing power, datasets, knowledge, and experience to promote the development of youth artificial intelligence education, showcasing excellent results internationally, and helping China become a major innovation center for artificial intelligence in the world.

Generative Adversarial Networks: Intuitive Principles and Simple Applications
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Source: Beijing Normal University Institute of Intelligent Learning Research

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