Generative Adversarial Networks (4) The enthusiastic learner and writer, this yellow duck is back online. Last time, we discussed the network structure of ACGAN, the method of introducing category information, the metrics for evaluating the performance of generative adversarial networks, methods to improve model training stability, and more. In the ACGAN paper, there is a … Read more
Source: Information Network Engineering Research Center This article is 1000 words long and is recommended to be read in 5 minutes. This article will help you understand GAN, DCGAN, WGAN, and SRGAN. GAN The generative network receives random noise and generates realistic images; The discriminative network receives an image and generates the probability that the … Read more
Advancements in GAN: CGAN, DCGAN, WGAN, WGAN-GP, LSGAN, BEGAN In the previous article, we introduced the principles of GAN (Introduction to Generative Adversarial Networks). The Generative Adversarial Network (GAN) mainly consists of two parts: the Generator and the Discriminator. The idea of the Generative model G is to package a random noise into a realistic … Read more
Author: Mirko Translated by: Sour Bun Wishing you a peaceful Dragon Boat Festival Generative Adversarial Network Applications in Quantitative Investing Series (Part 1) Get the complete code at the end of the article 1 Introduction Overfitting is one of the challenges we face when applying machine learning techniques to time series. This issue arises because … Read more