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What is Generative Adversarial Network (GAN)

Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education
A powerful machine learning technique made up of two learning systems that compete with each other in a game-like fashion. Features of the winning system are “genetically” added to the loser along with random mutations. GANs teach themselves through this “survival of the fittest” evolutionary model. They “generate” new solutions through many, often millions, of generations.
Published in Chapter:
Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools
Scott R. Garrigan (Lehigh University, USA)
DOI: 10.4018/978-1-7998-1479-5.ch003
Computational thinking (CT) K-12 curricula and professional development should prepare students for their future, but historically, such curricula have limited success. This chapter offers historical analogies and ways that CT curricula may have a stronger and more lasting impact. Two frameworks are central to the chapter's arguments. The first recalls Seymour Papert's original description of CT as a pedagogy with computing playing a formative role in young children's thinking; the computer was a tool to think with (1980, 1996). This “thinking development” framework emphasized child-centered, creative problem solving to foster deep engagement and understanding. Current CT seems to include creativity only tangentially. The second framework encompasses emergent machine learning and data concepts that will become pervasive. This chapter, more prescriptive than empirical, suggests ways that CT and requisite professional development could be more future-focused and more successful. It could be titled “Seymour Papert meets Machine Learning.”
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Professionally Ethical Ways to Harness an Art-Making Generative AI to Support Innovative Instructional Design Work
A type of AI program that is comprised of a generator and a discriminator, which work in adversarial and competitive ways.
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Plant Disease Classification Using Deep Learning Techniques
GAN is used in unsupervised machine learning to generate new data samples that are similar to a given dataset. It consists of two parts: a generator network that creates new samples, and a discriminator network that evaluates the authenticity of the generated samples.
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Next Generation of Intelligent Cities: Case Studies From Europe
It refers to a type of neural network that consists of a generative and a discriminative network that contest with each other especially in a game scenario. They are used to generate new data that are statistically similar to the training data.
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