Advanced Deep Learning with Keras

Chapter 3
Conclusion
In this chapter, we've been introduced to autoencoders, which are neural
networks that compress input data into low-dimensional codes in order to
efficiently perform structural transformations such as denoising and colorization.
We've laid the foundations to the more advanced topics of GANs and VAEs, that
we will introduce in later chapters, while still exploring how autoencoders can
utilize Keras. We've demonstrated how to implement an autoencoder from two
building block models, both encoder and decoder. We've also learned how the
extraction of a hidden structure of input distribution is one of the common tasks
in AI.
Once the latent code has been uncovered, there are many structural operations
that can be performed on the original input distribution. In order to gain a better
understanding of the input distribution, the hidden structure in the form of the
latent vector can be visualized using low-level embedding similar to what we did
in this chapter or through more sophisticated dimensionality reduction techniques
such t-SNE or PCA.
Apart from denoising and colorization, autoencoders are used in converting
input distribution to low-dimensional latent codes that can be further processed
for other tasks such as segmentation, detection, tracking, reconstruction, visual
understanding, and so on. In Chapter 8, Variational Autoencoders (VAEs), we will
discuss VAEs which are structurally the same as autoencoder but differ by having an
interpretable latent code that can produce a continuous latent codes projection. In the
next chapter, we will embark on one of the most important recent breakthroughs in
AI, the introduction of GANs where we will learn of the core strengths of GANs and
their ability to synthesize data or signals that look real.
References
1. Ian Goodfellow and others. Deep learning. Vol. 1. Cambridge: MIT press,
2016 (http://www.deeplearningbook.org/).
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