Yann LeCun described it as "the most interesting idea of machine learning in the past 10 years." Of course, this compliment from a distinguished researcher in the field of deep learning is always a good advertisement for the topic we are talking about!
generateConfrontation networkIntroduction to GANs for beginners, principle explanation-with code
For a while, NVIDIA's StyleGANIt can be said that it is a fire, and recently made a big move! In the past, image-to-image conversion required a large number of images for training samples, but in this work of Nvidia, image-to-image conversion can be done with only a small sample (the code is open source)!
Read the generative confrontational network (GANResearch and evaluate how to develop and then implement your own GAN to generate handwritten numbers
This article isGANAs part of the series, we will study why training is so elusive. Through this research, we learned some basic questions that drive the direction of many researchers. We will study some differences so that we know where the research might go. Before studying these issues, let's quickly review the GAN equation.
Before 5, GANs began a revolution in deep learning. This revolution has produced some major technological breakthroughs. This article will introduce in detail GAN History and the effects of different models.
Compared to other fields, machine learning/artificial intelligence now seems to have a super-funny development with higher frequency.
Researchers at universities such as Zhejiang University and Sydney University proposed MirrorGAN, as a global-local attention and semantically maintained text-image-text framework, addresses semantic consistency between textual descriptions and visual content, and refreshes records on COCO datasets.
The generation of anti-neural networks (GANs) is the key to the further development of deep learning, and it has great application prospects in many fields. But the prosperity of GANs also needs to cross the two mountains of hardware and framework.
TorchGAN It is based on PyTorch's GAN design development framework. The framework is designed to provide building blocks for popular GANs and allows for customization of cutting-edge research.