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Generative Adversarial Networks
We suggest you experiment with other datasets in the TensorFlow CycleGAN
datasets. Some will be easy like apples and oranges, but some will require much
more training. The authors also maintain a GitHub repo where they have shared
their own implementation in PyTorch along with the links to implementations in
other frameworks including TensorFlow: https://github.com/junyanz/CycleGAN.
Summary
This chapter explored one of the most exciting deep neural networks of our times:
GANs. Unlike discriminative networks, GANs have an ability to generate images
based on the probability distribution of the input space. We started with the first
GAN model proposed by Ian Goodfellow and used it to generate handwritten digits.
We next moved to DCGANs where convolutional neural networks were used to
generate images and we saw the remarkable pictures of celebrities, bedrooms, and
even album artwork generated by DCGANs. Finally, the chapter delved into some
awesome GAN architectures: the SRGAN, CycleGAN, and InfoGAN. The chapter
also included an implementation of the CycleGAN in TensorFlow 2.0.
In this chapter and the ones before it we have been largely concerned with images;
the next chapter will move into textual data. You will learn about word embeddings
and learn to use some of the recent pretrained language models for embeddings.
References
1. Goodfellow, Ian J. On Distinguishability Criteria for Estimating Generative
Models. arXiv preprint arXiv:1412.6515 (2014). (https://arxiv.org/
pdf/1412.6515.pdf)
2. Dumoulin, Vincent, and Francesco Visin. A guide to convolution arithmetic for
deep learning. arXiv preprint arXiv:1603.07285 (2016). (https://arxiv.org/
abs/1603.07285)
3. Salimans, Tim, et al. Improved Techniques for Training GANs. Advances in
neural information processing systems. 2016. (http://papers.nips.cc/
paper/6125-improved-techniques-for-training-gans.pdf)
4. Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. Perceptual Losses for Real-
Time Style Transfer and Super-Resolution. European conference on computer
vision. Springer, Cham, 2016. (https://arxiv.org/abs/1603.08155)
5. Radford, Alec, Luke Metz, and Soumith Chintala. Unsupervised Representation
Learning with Deep Convolutional Generative Adversarial Networks. arXiv
preprint arXiv:1511.06434 (2015). (https://arxiv.org/abs/1511.06434)
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