Mit Sammlungen den Überblick behalten
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Generative Adversarial Networks (GANs) sind eine spannende Innovation im Bereich maschinelles Lernen. GANs sind generative Modelle: Sie erstellen neue Dateninstanzen, die Ihren Trainingsdaten ähneln. GANs können beispielsweise Bilder erstellen, die wie Fotos von menschlichen Gesichtern aussehen, obwohl die Gesichter keiner realen Person gehören. Diese Bilder wurden von einem GAN erstellt:
GANs erreichen diesen Realismus, indem sie einen Generator, der lernt, die Zielausgabe zu erzeugen, mit einem Diskriminator kombinieren, der lernt, echte Daten von der Ausgabe des Generators zu unterscheiden. Der Generator versucht, den Diskriminator zu täuschen, und der Diskriminator versucht, sich nicht täuschen zu lassen.
In diesem Kurs werden die Grundlagen von GANs und die Verwendung der TF-GAN-Bibliothek zum Erstellen von GANs behandelt.
[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Benötigte Informationen nicht gefunden","missingTheInformationINeed","thumb-down"],["Zu umständlich/zu viele Schritte","tooComplicatedTooManySteps","thumb-down"],["Nicht mehr aktuell","outOfDate","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Problem mit Beispielen/Code","samplesCodeIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],["Zuletzt aktualisiert: 2025-02-26 (UTC)."],[[["\u003cp\u003eGenerative adversarial networks (GANs) are generative models that create new data instances resembling training data, such as images that look like real photographs but are not of actual people.\u003c/p\u003e\n"],["\u003cp\u003eGANs consist of a generator that learns to produce the target output and a discriminator that learns to distinguish real data from generated data, working in tandem to enhance the realism of the output.\u003c/p\u003e\n"],["\u003cp\u003eThis course covers GAN fundamentals, common GAN loss functions, training challenges, and using the TF-GAN library to build GANs, assuming prior knowledge of machine learning and TensorFlow.\u003c/p\u003e\n"],["\u003cp\u003eCompleting Machine Learning Crash Course and having some TensorFlow programming experience are prerequisites for this GANs course.\u003c/p\u003e\n"]]],[],null,["# Introduction\n\n\u003cbr /\u003e\n\nGenerative adversarial networks (GANs) are an exciting recent innovation in\nmachine learning. GANs are *generative* models: they create new data instances\nthat resemble your training data. For example, GANs can create images that look\nlike photographs of human faces, even though the faces don't belong to any real\nperson. These images were created by a GAN:\n\n**Figure 1: Images generated by a\n[GAN created by NVIDIA](https://research.nvidia.com/sites/default/files/pubs/2017-10_Progressive-Growing-of/karras2018iclr-paper.pdf).**\n\nGANs achieve this level of realism by pairing a generator, which learns to\nproduce the target output, with a discriminator, which learns to distinguish\ntrue data from the output of the generator. The generator tries to fool the\ndiscriminator, and the discriminator tries to keep from being fooled.\n\nThis course covers GAN basics, and also how to use the TF-GAN library to create\nGANs.\n| **Course Learning Objectives**\n|\n| - Understand the difference between generative and discriminative models.\n| - Identify problems that GANs can solve.\n| - Understand the roles of the generator and discriminator in a GAN system.\n| - Understand the advantages and disadvantages of common GAN loss functions.\n| - Identify possible solutions to common problems with GAN training.\n| - Use the TF GAN library to make a GAN.\n\nPrerequisites\n-------------\n\nThis course assumes you have:\n\n- Completed [Machine Learning Crash Course](/machine-learning/crash-course) either in-person or self-study.\n- At least a little experience programming with [TensorFlow](https://tensorflow.org)"]]
