Advanced Deep Learning with Keras

Variational Autoencoders (VAEs)
The Keras code for β -VAE has pre-trained weights. To test β -VAE with β = 7
generating digit 0, we need to run:
$ python3 cvae-cnn-mnist-8.2.1.py --beta=7 --weights=beta-cvae_cnn_mnist.
h5 --digit=0
Conclusion
In this chapter, we've covered the principles of variational autoencoders (VAEs).
As we learned in the principles of VAEs, they bear a resemblance to GANs in the
aspect of both attempt to create synthetic outputs from latent space. However, it can
be noticed that the VAE networks are much simpler and easier to train compared to
GANs. It's becoming clear how conditional VAE and β -VAE are similar in concept to
conditional GAN and disentangled representation GAN respectively.
VAEs have an intrinsic mechanism to disentangle the latent vectors. Therefore,
building a β -VAE is straightforward. We should note however that interpretable
and disentangled codes are important in building intelligent agents.
In the next chapter, we're going to focus on Reinforcement learning. Without any
prior data, an agent learns by interacting with its world. We'll discuss how the
agent can be rewarded for correct actions and punished for the wrong ones.
References
1. Diederik P. Kingma and Max Welling. Auto-encoding Variational Bayes. arXiv
preprint arXiv:1312.6114, 2013(https://arxiv.org/pdf/1312.6114.pdf).
2. Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning Structured Output
Representation Using Deep Conditional Generative Models. Advances in
Neural Information Processing Systems, 2015(http://papers.nips.cc/
paper/5775-learning-structured-output-representation-usingdeep-conditional-generative-models.pdf).
3. Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation Learning:
A Review and New Perspectives. IEEE transactions on Pattern Analysis
and Machine Intelligence 35.8, 2013: 1798-1828(https://arxiv.org/
pdf/1206.5538.pdf).
4. Xi Chen and others. Infogan: Interpretable Representation Learning by Information
Maximizing Generative Adversarial Nets. Advances in Neural Information
Processing Systems, 2016(http://papers.nips.cc/paper/6399-
infogan-interpretable-representation-learning-by-informationmaximizing-generative-adversarial-nets.pdf).
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