Gan neural network

Gan neural network. However, images from existing datasets do not represent real-world scenarios well enough in terms of view variations and data distributions Satellite Image Prediction Relying on GAN and LSTM Neural Networks Zhan Xu , Jun Du , Jingjing Wang , Chunxiao Jiangyz and Yong Ren Mar 29, 2017 · GAN by Example using Keras on Tensorflow Backend. Mar 7, 2019 · The adversarial loss is based on the GAN loss in the original GANs [23] that are neural networks trained by playing a min–max game. Source. The training procedure for G is to maximize the probability of D making a mistake. Sep 1, 2023 · Even though the proposed CrackSegAN is a GAN-based neural network, a large-scale dataset is necessary to train the network. Jan 7, 2019 · A Generative Adversarial Network (GAN) is a type of machine learning model that uses two neural networks: a generator and a discriminator Jun 16, 2016 · One clever approach around this problem is to follow the Generative Adversarial Network (GAN) approach. You can feed it a little bit of random noise as input, and it can produce realistic images of bedrooms, or birds, or whatever it is trained to generate. Here we introduce a second discriminator network (usually a standard convolutional neural network) that tries to classify if an input image is real or generated. Discover the training algorithm used to train all GAN models in an adversarial two-player game. One thing all scientists can agree on is that we need more data. , noise). 1 to 10 −6 (each time decrease by 10-fold) and batch normalization Mar 1, 2023 · A GAN structure works as a two-part deep neural network designed to receive actual data (True samples) and generate synthetic data (Fake samples) with the most similarity to the real ones. GAN Hacks Jul 4, 2022 · Generative Adversarial Networks (GANs) was first introduced by Ian Goodfellow in 2014. Generative Adversarial Networks (GAN) is one of the most promising recent developments in Deep Learning. May 17, 2023 · Protection of computer systems and networks against malicious attacks is particularly important in industrial networked control systems. WGAN focuses on A generative adversarial network (GAN) is a machine learning ( ML) model in which two neural networks compete with each other by using deep learning methods to become more accurate in their predictions. Jul 18, 2022 · generator network, which transforms the random input into a data instance; discriminator network, which classifies the generated data; discriminator output; generator loss, which penalizes the generator for failing to fool the discriminator; Figure 1: Backpropagation in generator training. The generator tries to fool the discriminator by generating real-looking images while the discriminator tries to distinguish between real and fake images. As you can see, there are two pieces in GAN Jun 30, 2020 · A generative adversarial network (GAN) is a class of machine learning frameworks conceived in 2014 by Ian Goodfellow and his colleagues. The forger is known as the generative network, and is also typically a convolutional neural network (with deconvolution layers). These work together to provide 1. The generative approach is an unsupervised learning method in machine Jun 26, 2020 · GAN neural networks training. in 2014. Networks: Deep neural networks, which are artificial intelligence (AI) systems, are used for training. GAN can learn the generative model of any data distribution through adversarial methods with excellent Jun 10, 2014 · We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. Learn about the different aspects and intricacies of generative adversarial networks (GAN), a type of neural network that is used both in and outside of the artificial intelligence (AI) space. WGAN) where we have implemented an unsupervised In this article, a scalable knowledge-based neural network (KBNN) large-signal model of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with accurate trapping and self-heating effects characterization is developed. Apr 12, 2023 · Convolutional neural networks (CNNs) and generative adversarial networks (GANs) are examples of neural networks-- a type of deep learning algorithm modeled after how the human brain works. The reason for choosing the word “adversarial” in GAN is that these two networks are in constant conflict throughout the training process. Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years A GAN is a type of neural network that is able to generate new data from scratch. Two neural networks (Generator and Discriminator) compete with each other like in a game. GAN, introduced by Ian Goodfellow in 2014, attacks the problem of unsupervised learning by training two deep networks, called Generator and Discriminator, that compete and cooperate with each Jan 18, 2021 · The Pix2Pix Generative Adversarial Network, or GAN, is an approach to training a deep convolutional neural network for image-to-image translation tasks. Despite the improvement accomplished in GANs in the Wasserstein GAN. For example, generative networks trained on photographs of human faces can generate realistic-looking faces which are entirely fictitious. To understand GANs first you must have little understanding of Convolutional Neural Networks. GANs, which can be used to produce new data in Jul 21, 2022 · By Caper Hansen. For the sake of simplicity let us call the output images of the generator network as fake images. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person. g. [5]. GNNs are used in predicting nodes, edges, and graph-based tasks. Super-Resolution GAN (SRGAN) SRGANs address image super-resolution, which involves enhancing image quality and resolution. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of May 11, 2020 · The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Graph Neural Networks are special types of neural networks capable of working with a graph data structure. Jun 11, 2020 · 2. biz/BdvxDJGenerative Adversarial Networks (GANs) pit two different deep learning models against each other in a game. The trainnet function does not support training GANs, so you must implement a custom training loop. Jul 3, 2023 · Introduction. A generator model is capable of generating new artificial samples that plausibly could have come from an existing distribution of samples. Generative modeling involves using a model to generate new examples that plausibly come from an existing distribution of samples, such as generating new photographs that are similar but specifically different from a dataset of existing photographs. Jul 1, 2021 · T-GAN is a deep neural network model consisting of an encoder and decoder, which integrates the topological structure of complex networks with the extensive feature information of vertices for learning and modeling the evolutionary property of temporal networks. Introduction. Repeat steps 1 and 2 to continue to train the generator and discriminator networks. Jul 18, 2020 · Deep Convolutional GAN (DCGAN): This an extension to replace the feed forward neural network with a CNN architecture proposed by A. This allows us to improve the data generator until it generates something that resembles the real data. Aug 23, 2023 · GAN presents an example of an algorithm-level competitive parallel model. Feb 28, 2024 · Generative Adversarial Networks (GANs) were developed in 2014 by Ian Goodfellow and his teammates. A GAN achieves this feat by training two models simultaneously. github. The input space is represented as a uniform square grid. Neural networks need some form of input. Hence, we understand the term “adversarial” in “generative Generative adversarial networks (GAN) are a class of generative machine learning frameworks. The basic augmentation methods are limited in terms of the . First, 3018 groups of sample data composed of device structure A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images. GAN-Holo architecture for generating hologram can be defined as follows: GAN-Holo is a deep convolutional neural network architecture, which consists of two networks including generator (G) and discriminator (D) as shown in Figure 3(a), competing against each other. Wasserstein GAN (WGAN): WGAN is designed by M. Generative: A generative model specifies how data is created in terms of a probabilistic model. The GAN loss is defined as (2) min G max D E [log D (Y) + log (1 − D (G (X)))] In order to alleviate the instability of GAN during training, a conditional item [25] is introduced in the discriminative sub-network. This paper studies low-light image enhancement algorithms, aiming to improve the quality of low-light images by studying some technical Aug 8, 2023 · RNNs are better suited to analyzing temporal and sequential data, such as text or videos. A generator and a discriminator are both present in GANs. The discriminator guides our generator network. Copy Command. Apr 3, 2020 · Overall architecture. About GANs. To start (Step 1), we randomly generate a vector (i. They are highly influenced by Convolutional Neural Networks (CNNs) and graph embedding. How is that done? 👀. This supports the generation of high-resolution and realistic images. As discriminator training tries to figure out how to distinguish real data from fake, it has to learn how to recognize the generator's flaws. Since Conditional GAN is a type of GAN, you will find it under the Generative Adversarial Networks subcategory. The two neural networks must have a similar “skill level. GANs are generative models: they create new data instances that resemble your training data. 2. In GAN Lab, a random input is a 2D sample with a (x, y) value (drawn from a uniform or Gaussian distribution), and the output is also a 2D sample, but mapped into a different position, which is a fake sample. Dec 30, 2022 · The GAN I created for experimenting with MNIST image dataset in the MP-GAN infrastructure has the following structure: The discriminator (Fig. Because of this and their unique approach to Machine Learning, I have given Neural Networks their own category in my ML Universe chart. While we would want the losses to decrease in conventional Neural Networks, in conditional GANs, we want the Generator and Discriminator to be adversaries. GANs was designed in 2014 by a computer scientist and engineer, Ian Goodfellow, and some of his colleagues. An improved empirical drain current model is proposed and added to the neural network as prior knowledge, thereby establishing the drain current model, including Feb 1, 2024 · Being an Adversarial Neural Network, Sea-Pix-GAN ’s loss function is different than conventional Neural Networks. I A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data. :label:fig_gan. The two entities are Generator and Discriminator. , rotation, flipping, cropping, color spacing); and (2) deep learning approaches (e. The newly generated data set appears similar to the training data sets. Therefore, a discriminator that receives the delta Mar 29, 2022 · Deep underwater color images have problems such as low brightness, poor contrast, and loss of local details. Three approaches employing This may be mitigated by the nets’ respective learning rates. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. This technique learns to generate new data using the same statistics as that of the training set, given a training set. Training Algorithm. The Generative Adversarial Networks (GANs) can be described as extremely powerful kinds of neural networks that are employed to aid in Unsupervised Learning. def define_gan(g_model, d_model): d_model. This example shows how to train a generative adversarial network to generate images. GAN is an architecture of Deep Learning. A Generative Adversarial Network or GAN is defined as the technique of generative modeling used to generate new data sets based on training data sets. For example, GAN architectures can generate fake, photorealistic pictures of animals or people. Once we reach the output layer, the discriminator decides whether the sample is real or fake (generated). GAN is basically an approach to generative modeling that generates a new set of data based on training data that look like training data. CNNs are feedforward neural networks that use filters and pooling layers, whereas RNNs feed results back into the network. The [] The GAN framework naturally takes up a game-theoretic approach. We’ll use TensorFlow, a deep learning library open-sourced by Google that makes it easy to train neural networks on GPUs. The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches". Understanding them means mastering the surprising power of playing a computer out against itself. Part of a series on. For generating a key between two parties, asymmetric cryptography is commonly used to exchange the key over an unprotected medium. A GAN is [] A generative adversarial network (GAN) uses two neural networks, called a generator and discriminator, to generate synthetic data that can convincingly mimic real data. ‘I’m done!’. GANs are comprised of both generator and discriminator models. In CNNs, the size of the input and the resulting output are fixed. Existing methods for detecting GAN-generated faces can perform well on limited public datasets. Generative adversarial networks (GANs) are a set of deep neural network models used to produce synthetic data. GANs usually containing two neural networks to train and compete against each other: one generator and one discriminator. Oct 25, 2020 · The principle is a two-player game: a neural network called the generator and a neural network called the discriminator. We then sample authentic images from our training set and mix them with our synthetic images (Step 3). While certain existing methodologies excel in specific aspects, such as achieving high PSNR values, they might falter in terms of MSE or SSIM. We have optimized the hyper-parameters for training the GANs by setting the learning rate from 0. The idea of using a CNN architecture and learning through filters have improved the accuracy of GAN models. This tutorial expects that you’re already at least a little bit familiar with Aug 16, 2022 · A Generative Adversarial Network (GAN) is a machine learning framework consisting of two neural networks competing to produce more accurate predictions such as pictures, unique music, drawings, and so on. They were introduced by Ian Goodfellow et al. It’s a type of machine learning model called a neural network, specially designed to imitate the structure and function of a human brain. The generator is responsible for generating new samples [] Aug 27, 2021 · generator = define_generator(100) We are using a couple of Dense layers to define the generator model with again leaky relu as an activation function in hidden layers and tanh in the final layer. For instance, we could feed the 200 generated images and 200 real images into Generative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce. The role of G is to create data in such a way so that it can fool the Jan 12, 2023 · GaN JBS diodes exhibit excellent performance in power electronics. The generated images G (z) will be of the shape 28x28x1. One way to visualize this mapping is using manifold [Olah, 2014]. This deep ANN contains two different neural components: generator and discriminator. Jun 27, 2022 · The data goes through the network with one or multiple hidden layers, the same as what you would have in any other neural network. " In my own words, GANs are composed of Jan 27, 2021 · TGAN or Time-series Generative Adversarial Networks, was proposed in 2019, as a GAN based framework that is able to generate realistic time-series data in a variety of different domains, meaning, sequential data with different observed behaviors. We pass this noise through our generator, which generates an actual image (Step 2). a picture of a human face) that appear to be a sample from the distribution of the training set (e. Arjovsky et al. CNNs, one of the oldest and most popular of the deep learning models, were introduced in the 1980s and are often used in visual recognition tasks. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and Jun 12, 2020 · Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. GANs mainly contain two neural networks capable of capturing, copying, and analyzing the variations in a dataset. Unofficial implementation of Satellite Image Prediction Relying on GAN and LSTM Neural Networks, Author : Zhan Xu∗ , Jun Du∗ , Jingjing Wang∗ , Chunxiao Jiang and Yong Ren∗ Original Paper : Satellite Image Prediction Relying on GAN and LSTM Neural Networks Jul 14, 2023 · Generative Adversarial Network: A generative adversarial network (GAN) is a type of construct in neural network technology that offers a lot of potential in the world of artificial intelligence. Code: http://www. Random Input. A generative adversarial network (GAN) is a type of deep learning network that can generate data with similar characteristics as the input real data. Discover the specialized layers used in convolutional neural networks required for image generation. This Dec 14, 2021 · Generative Antagonistic Networks (GAN) is a barbaric name for Deep Learning models that generate information, images, music. GANs have been an active topic of research in recent years. Published July 21, 2022. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion). GAN utilizes two neural networks with competitive learning approach: (1) a Generator to produce a new data from the input noise and (2) a Discriminator to discriminate this Nov 30, 2023 · Comparing the existing methodologies with the deep neural network and GAN-based approach, it becomes evident that the methodology offers a more robust performance across all three metrics. Therefore, in this study, an anomaly detection neural network, dual auto Jul 12, 2019 · Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset. Jun 7, 2017 · We’re going to create a GAN that will generate handwritten digits that can fool even the best classifiers (and humans too, of course). [6]. A successful cyber-attack may cause significant economic losses or even destruction of controlled processes. com/luisguiserrano/gansWhat is the simplest pair of GANs one can build? In this video (with code included) we build a pair of ONE-lay Nov 22, 2019 · Generative Adversarial Networks. At the very least, it needs to fool the classifier even if our classifier is a state of the art deep neural network. com Jan 29, 2024 · What is GAN? GAN stands for Generative Adversarial Network. The standard way to inject conditions in GANs cannot work without concern for the problem of generation conditioned by deltas: as I observed in some indepedent experiments, causal convolution-based neural networks are able to easily solve the problem of detecting the delta of a given sequence. GANs take a long time to train. Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Here comes the Discriminator, a regular neural network classifier. The shop owner in the example is known as a discriminator network and is usually a convolutional neural network (since GANs are mainly used for image tasks) which assigns a probability that the image is real. Dec 30, 2022 · With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. The discriminator can be any image classifier, even a decision tree. https:/ Aug 1, 2022 · While most types of Neural Networks are Supervised, some, like Autoencoders, are Self-Supervised. Aug 2, 2023 · Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The careful configuration of architecture as a type of image-conditional GAN allows for both the generation of large images compared to prior GAN models (e. Convolutional neural networks are widely used in computer vision and have become the state of the art for many visual applications such as image classification, and have also found success in natural language processing for text classification. Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. May 10, 2020 · The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture to give control over the disentangled style properties of generated images. GANs have two main blocks (two neural networks) which compete with each other and are able to capture, copy Feb 1, 2018 · An adversarial neural network, often referred to as an “adversarial network” or simply “GAN” (Generative Adversarial Network), is a type of 2 min read · Oct 27, 2023 Papers in 100 Nov 11, 2021 · Learn more about watsonx: https://ibm. 9) is a CNN (convolutional neural network) with two convolutions separated by dropout layers, ending with a Dense layer with a sigmoid activation. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. , Generative Adversarial Network(GAN), Neural Style Transfer) . This article walks you through an introduction, describes what GANs are, and explains how you can use them. However, as the approaches that used this technique, such as RSA, have been compromised, new ways to produce a key that can provide protection must be found. Radford et al. The generator (Fig. Aug 3, 2022 · A generative adversarial network, or GAN, is a deep neural network framework that can learn from training data and generate new data with the same characteristics as the training data. On a single GPU a GAN might take hours, and on a single CPU more than a day. trainable = False. We do not want any player defeating the other for optimal training. GANs can create anything whatever you feed to them, as it Learn-Generate-Improve. GANs are somewhat similar to variational autoencoders (VAEs) in the sense that both systems generate synthetic data, however, GANs are significantly more Jun 28, 2021 · Generative Adversarial Networks (GANs) are Neural Networks that take random noise as input and generate outputs (e. The goal of a GAN is to train a discriminator to be able to distinguish between real and fake data while Sep 18, 2020 · Generator networks see many images and try to output something similar to the same probability distribution. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. GANs are most often used with image data but GANs can create any type of data. 10) starts with a dense layers Neural network (machine learning) An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. 3. Thus, further research can be done in developing unsupervised or semi-supervised GAN, which can address different types of cracks under different scenarios without large-scale manually labeled datasets. ake any course on machine learning and you’ll invariably encounter Generative Adversarial Networks, or GANs. From Wikipedia, "Generative Adversarial Networks, or GANs, are a class of artifical intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework. e. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to Discover the life cycle for developing a deep learning neural network model using the Keras library. The generative models considered in this work, GANs, 10,11 employ two neural networks - a generator and a discriminator - to learn random distributions that are Jun 13, 2021 · Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to its ability to generate high-quality data of significant statistical resemblance to real data. Generative adversarial networks (GAN) are a class of generative machine learning frameworks. Generative adversarial networks (GANs), is an algorithmic architecture that consists of two neural networks, which are in competition with each other (thus the “adversarial”) in order to generate new, replicated instances of data that can pass for real data. CNNs are used for image classification. Dec 26, 2019 · Their proposed system — GAN-TTS — consists of a neural network that learned to produce raw audio by training on a corpus of speech with 567 pieces of encoded phonetic, duration, and pitch data Oct 15, 2023 · One technique that adds more information to the original dataset is data augmentation, which includes: (1) basic image augmentation (e. Our generator starts from a learned constant input and adjusts the “style” of the image at each convolution layer based on the latent code, therefore directly Nov 16, 2020 · Most GANs are trained using a six-step process. While difficult to tune and therefore to use, GANs have stimulated a lot of interesting research and writing. Sep 1, 2020 · Generative Adversarial Networks, or GANs for short, are a deep learning architecture for training powerful generator models. It’s around five o’clock and you’ve just finished your homework. The Generative Adversarial Network consists of two neural networks. In order to effectively enhance low-quality underwater images, this paper proposes an enhancement method based on GAN (Generative Adversarial Network). Mar 16, 2020 · In this video, I give an overview of Generative Adversarial Networks and walk through the code of a project I developed to produce videos using GAN's. These networks are competing with each other in a zero-sum game framework. The GAN architecture is comprised of both a generator and a discriminator model. In this study, we used a neural network machine learning method to predict the performance of a GaN JBS diode. They use convolutional layers in both the generator and discriminator. To Jun 2, 2021 · A generative adversarial network (GAN) is a deep neural system that can be used to generate synthetic data. A generative adversarial network is composed of two neural networks: a generative network and a discriminative network. Traditional GANs employ deep neural architectures to produce realistic images in order to perform an intelligent system involving two deep neural networks (DNNs): a generator “G”, and a discriminator “D”. May 10, 2018 · Now that I have whetted your appetite, let move on to what a GAN is and how it works. For example, a generative adversarial network trained on photographs of human faces can generate realistic-looking faces which are entirely Jun 13, 2019 · A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling. The method was developed by Ian Goodfellow in 2014 and is outlined in the paper Generative Adversarial Networks. Adversarial: The model is trained in an adversarial environment. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. Jan 30, 2020 · Jan 30, 2020. such as 256x256 pixels) and the capability of performing well on a variety of different Apr 1, 2022 · In this paper, a GAN-based optimal neural network structure for group synchronization is proposed. This Jul 18, 2022 · The generator trains for one or more epochs. We use a convolutional neural network instead, with 4 blocks of layers. ” 1. For this reason, neural networks in machine learning are sometimes referred to as artificial neural networks (ANNs). Therefore, it is necessary to test the vulnerability of process control industrial networks against possible cyber-attacks. Jul 18, 2022 · Introduction. Different from other GAN architectures (eg. Generative Adversarial Networks takes up a game-theoretic approach, unlike a conventional neural network. Jul 12, 2019 · Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. Apr 20, 2020 · Deep Convolutional Generative Adversarial Networks (DCGANs) are GANs that use convolutional layers. May 16, 2020 · GAN is essentially a neural network architecture (more of a framework than an architecture), wherein a generative model is paired with a non-generative model; the generative model is penalized for a bad quality generation, whereas, the other model is penalized for a good quality generation by the generative model. We keep the generator constant during the discriminator training phase. The two DNNs are met in a zero-sum game. Upsampling Layers. See full list on machinelearningmastery. set of other human faces). PyTorch is a leading open source deep learning framework. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn, where one person's gain equals another person's loss. GAN Model DCGANs incorporate deep convolutional neural networks (CNNs) into GAN architectures. In summary, the discriminator is no different from a standard neural network classification model. It is a type of Neural Network which we have not yet discussed at Inside Machine Learning and yet, it has an increasingly important place in scientific research in Artificial Intelligence. CNNs and RNNs have different architectures. Mar 8, 2022 · Generative Adversarial Network (GAN) based techniques can generate and synthesize realistic faces that cause profound social concerns and security problems. However, device performance is affected by multiple parameters of the P+ region, and the traditional TCAD simulation method is complex and time-consuming. The GAN architecture is illustrated in :numref:fig_gan. The network learns to generate from a training distribution through a 2-player game. GANs are a powerful class of neural networks that are used for unsupervised learning. The Discriminator. sl vh dl oc io zl ul ok kf yu