{"id":171339,"date":"2023-09-25T17:47:12","date_gmt":"2023-09-25T16:47:12","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=171339"},"modified":"2023-10-30T20:19:04","modified_gmt":"2023-10-30T19:19:04","slug":"what-is-a-conditional-generative-adversarial-network-cgan","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/what-is-a-conditional-generative-adversarial-network-cgan","title":{"rendered":"What is a Conditional Generative Adversarial Network (cGAN)?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"171339\" class=\"elementor elementor-171339\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-42f22030 e-flex e-con-boxed e-con\" data-id=\"42f22030\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6a4047cb elementor-widget elementor-widget-text-editor\" data-id=\"6a4047cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2>The Conditional Generative Adversarial Network (cGAN) is a model used in deep learning, a derivative of machine learning. It enables more precise generation and discrimination of images to train machines and allow them to learn on their own. The concept of cGAN was first published in 2014 by Mehdi Mirza and Simon Osindero.<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25ca0d5 elementor-widget elementor-widget-text-editor\" data-id=\"25ca0d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To <strong>understand what a cGAN is<\/strong>, you first need to become familiar with Deep Learning. This process involves feeding a computer program thousands of data points so that it can learn to recognize them. The <a href=\"https:\/\/datascientest.com\/en\/gan-machine-learning-putting-fictitious-faces-into-practice\">Generative Adversarial Network (GAN)<\/a> represents an initial training approach. It facilitates a dialogue between two networks: the generator and the discriminator.<\/p><p>On one side, the generator creates fake images that are supposed to be as realistic as possible, with the aim of deceiving the opposing network:<strong> the discriminator.<\/strong><\/p><p>On the other side, <strong>the discriminator<\/strong> observes images coming from both the <a href=\"https:\/\/datascientest.com\/en\/database-what-is-it\">generator and a database.<\/a> It must determine which images come from the database (and label them as real) and which images are generated by the generator (and are therefore fake).<\/p><p>The discriminator correctly classifies fakes as fakes and real images as real, receiving positive feedback. If it fails in its task, it receives negative feedback. Gradually, thanks to the Gradient Descent algorithm, it can determine the range of data that allows it to recognize a real image, learn from its mistakes, and improve. Thus, it progressively enhances its ability to create more relevant objects.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4bcfe474 e-flex e-con-boxed e-con\" data-id=\"4bcfe474\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-19904189 elementor-widget elementor-widget-heading\" data-id=\"19904189\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">The cGAN or how to maximize the performance of the generator and the discriminator<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2fb07381 elementor-widget elementor-widget-text-editor\" data-id=\"2fb07381\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>With a conditional <a href=\"https:\/\/datascientest.com\/en\/edit-your-photos-at-will-with-drag-your-gan\">GAN,<\/a> it&#8217;s possible to send more precise information, called class labels, to both the generator and the discriminator to guide their data generation. These pieces of information help specify the data produced by the generator and the discriminator, allowing them to arrive at the desired results more quickly.<\/p><p>The labels guide the generator&#8217;s production to generate more specific information. For example, instead of producing images of clothing in general, it will produce images of pants, jackets, or socks based on the provided label.<\/p><p>On the discriminator&#8217;s side, the labels help the network better distinguish between real images and the fake images provided by the generator, making it more efficient.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f353d3a elementor-widget elementor-widget-image\" data-id=\"7f353d3a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"483\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/conditional-gan-1024x618.jpg\" class=\"attachment-large size-large wp-image-171344\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/conditional-gan-1024x618.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/conditional-gan-300x181.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/conditional-gan-768x463.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/conditional-gan.jpg 1520w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-38af0fb9 e-flex e-con-boxed e-con\" data-id=\"38af0fb9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4b59404c elementor-widget elementor-widget-heading\" data-id=\"4b59404c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The cGAN and its multiple uses in the field of machine learning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aea3668 elementor-widget elementor-widget-text-editor\" data-id=\"aea3668\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"flex flex-grow flex-col gap-3 max-w-full\"><div class=\"min-h-[20px] flex flex-col items-start gap-3 overflow-x-auto whitespace-pre-wrap break-words\"><div class=\"markdown prose w-full break-words dark:prose-invert light\"><p>If you&#8217;re having trouble picturing the use of the conditional GAN, here are some examples that might help:<\/p><\/div><\/div><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-40552a5c elementor-widget elementor-widget-heading\" data-id=\"40552a5c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">1. Image-to-image translation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5ab5f1bf elementor-widget elementor-widget-text-editor\" data-id=\"5ab5f1bf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>cGANs<\/strong>, in particular, allow images to evolve by considering additional information, such as labels. cGANs have enabled the development of the Pix2Pix method, some applications of which include object reconstruction from edges, photo synthesis from label maps, and image colorization.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16a18198 elementor-widget elementor-widget-heading\" data-id=\"16a18198\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">2. Creating images from text<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3daa8dd elementor-widget elementor-widget-text-editor\" data-id=\"3daa8dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Thanks to cGANs, it&#8217;s possible to create high-quality photos based on text. Using text and the richness of its vocabulary enables the generation of much more precise synthetic images.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ef5053a elementor-widget elementor-widget-heading\" data-id=\"7ef5053a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">3. Video generation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1f949a1f elementor-widget elementor-widget-text-editor\" data-id=\"1f949a1f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In video, cGANs can also predict future frames of a video based on a selection of previous images.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27786183 elementor-widget elementor-widget-heading\" data-id=\"27786183\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">4. Face generation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1af46753 elementor-widget elementor-widget-text-editor\" data-id=\"1af46753\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>cGANs can be used to generate images of faces with specific attributes, such as hair or eye color.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7db27b85 e-flex e-con-boxed e-con\" data-id=\"7db27b85\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-49484701 elementor-widget elementor-widget-text-editor\" data-id=\"49484701\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>cGANs represent a particular advancement when compared to GANs. They enable deep learning systems to gain precision and efficiency, <a href=\"https:\/\/datascientest.com\/en\/learn-ai-everything-you-need-to-know\">marking a small revolution in the field of machine learning<\/a>. This advancement propelled the careers of its two inventors, Mehdi Mirza and Simon Osindero, who now work at DeepMind, a leading company in the sector.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3220708b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3220708b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/deep-learning-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Deep Learning Course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The Conditional Generative Adversarial Network (cGAN) is a model used in deep learning, a derivative of machine learning. It enables more precise generation and discrimination of images to train machines and allow them to learn on their own. The concept of cGAN was first published in 2014 by Mehdi Mirza and Simon Osindero. To understand [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":171342,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is a Conditional Generative Adversarial Network (cGAN)?<\/title>\n<meta name=\"description\" content=\"When it comes to a Conditional Generative Adversarial Network, most people do not know that this is a very..\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/what-is-a-conditional-generative-adversarial-network-cgan\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is a Conditional Generative Adversarial Network (cGAN)?\" \/>\n<meta property=\"og:description\" content=\"The Conditional Generative Adversarial Network (cGAN) is a model used in deep learning, a derivative of machine learning. 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