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Machine Learning

Computer Vision

Generative Adversarial Networks for Image Generation

Generative Adversarial Networks

This chapter gives an introduction to generative adversarial networks (gan), by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation.

We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images.

We then list some architecture-variant and loss-variant gans that remedy the above challenges.

We present two utilization examples of gans for real-world applications : data augmentation and face images generation.

Authors

Gilad Cohen, Raja Giryes

Related Topics

Generative adversarial networks (gans)

Architecture-variant and loss-variant gans

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