In recent years, Generative Adversarial Networks (GAN) have rapidly developed and become one of the main research directions in the field of machine learning. GAN is based on the idea of zero-sum games, where its generator and discriminator learn in opposition to capture the data distribution of given samples, generating new sample data. A large … Read more
Highlights: This article systematically reviews the application of a deep learning method—Generative Adversarial Networks (GANs)—in the auxiliary diagnosis of Alzheimer’s Disease (AD) and the processing of neuroimaging data (image denoising, image segmentation, data augmentation, and modality conversion); This article finds that compared to other methods, GANs exhibit higher classification accuracy in AD auxiliary diagnosis tasks … Read more
1 New Intelligence Column Author: Liu Shiqiang [New Intelligence Guide] This article introduces the application of deep learning methods in the field of image translation by implementing an encoding-decoding “image translator” for image enhancement, showcasing the effectiveness of deep learning applications in image translation. In recent years, deep learning has achieved remarkable results in image … Read more
[Brief Note] From July 17 to 18, 2017, the first session of the Frontier Lecture Series on Intelligent Automation, organized by the Chinese Association of Automation, was held in Beijing. To promote in-depth research on the theories, methods, technologies, and applications related to Generative Adversarial Networks (GAN), the first session invited several well-known scholars from … Read more
Click the “Expert Knowledge” above to follow for more AI knowledge! [Introduction] Thematic aggregation knowledge is one of the core functions of Expert Knowledge, providing users with systematic knowledge learning services in the field of AI. Thematic aggregation offers users a collection of the essence (Awesome) knowledge materials about the theme from the entire network, … Read more
Previously introduced were CNN (Convolutional Neural Network), BNN (Binarized Neural Network), dual-learning NMT and DBN, as well as deep learning optimization algorithms Batch Normalization and Layer Normalization. Students interested can add the WeChat public account “Deep Learning and NLP“, reply with keywords “CNN”, “BNN“, “dual”, “DBN“, BN and LN to get the corresponding article links. … Read more
Hello everyone, today we will discuss Generative Adversarial Networks, and how to create anime avatars. In this lesson, we will design and implement a Convolutional Generative Adversarial Network (DCGAN): Then we will use this network to generate various anime avatars. 1. What Are Generative Adversarial Networks Generative Adversarial Networks, abbreviated as GAN. GAN is an … Read more
Selected from KDnuggets Translated by Machine Heart Author:Michael Dietz Contributors: Jane W, Yan Qi, Wu Pan Generative Adversarial Networks (GANs) have gained significant attention in the research community recently. In this article, Michael Dietz, founder of Waya.ai, explains why GANs hold such potential and illustrates how GANs work through a vivid comparison with boxing matches. … Read more
In the previous article, someone raised the question in a comment about whether generative adversarial networks could be used to simulate shooting covers. This was previously tested in a simple experiment, but the results were not ideal. The unsatisfactory results stem from the implementation of general generative adversarial networks, which essentially involves a transformation or … Read more
