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You're reading from Advanced Deep Learning with Keras

Product typeBook

Published inOct 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788629416

Edition1st Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Learning

Author (1)

Rowel Atienza

Auxiliary classifier GAN (ACGAN)

ACGAN is similar in principle to the Conditional GAN (CGAN) that we discussed in the previous chapter. We're going to compare both CGANand ACGAN. For both CGAN and ACGAN, the generator inputs are noise and its label. The output is a fake image belonging to the input class label. For CGAN, the inputs to the discriminator are an image (fake or real) and its label. The output is the probability that the image is real. For ACGAN, the input to the discriminator is an image, whilst the output is the probability that the image is real and its class label. Following figure highlights the difference between CGAN and ACGAN during generator training:

Figure 5.3.1: CGAN vs. ACGAN generator training. The main difference is the input and output of the discriminator.

Essentially, in CGAN we feed the network with side information (label). In ACGAN, we try to reconstruct the side information using an auxiliary class decoder network. ACGAN argued that forcing the network to...

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Advanced Deep Learning with Keras

Published in: Oct 2018Publisher: PacktISBN-13: 9781788629416

Author (1)

Rowel Atienza

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).
Read more about Rowel Atienza

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Auxiliary classifier GAN (ACGAN)

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