Author: Zen and the Art of Computer Programming
1 Introduction
With the vigorous development of AI technology, the continuous improvement of models and the growth of data volume, image generation technology based on deep learning technology is becoming more and more popular. But how to use deep learning technology to synthesize "real person" style pictures is still a long-term challenge. Many people think that GAN (Generative Adversarial Networks) can be used to synthesize "real person" style pictures, but in fact there are still many limitations and difficulties. Therefore, this article will systematically summarize the existing image generation technologies and sort out their classifications, so as to better help readers understand the current research direction and grasp the future development direction and application prospects.
2. Explanation of basic concepts and terms
Before the introduction, some related basic concepts and terms are introduced. Since this article is aimed at machine learning engineers, algorithmic and programming details are temporarily ignored. If you need to know, you can refer to the relevant information by yourself.
Generative Model: A generative model is a probabilistic model that can generate new samples according to given conditions, which can be used to generate real or fake images, text, etc. The earliest generative models mainly include hidden Markov model (HMM) and Markov chain Monte Carlo method (MCMC). In recent years, deep learning-based generative models have developed rapidly, such as variational autoencoders (VAE), GANs, and generative adversarial networks (GANs).
Deep Learning: Deep learning is an important branch of machine learning, which refers to a data learning method composed of multi-layer neural networks. Deep learning has become a standard tool in image recognition, video analysis, word processing and other fields, and it is also crucial for image generation. The underlying mechanism of deep learning technology can extract high-level features and create new images by combining these features.
Graphical style conversion (Style Tra