Learn Python Generative AI by Zonunfeli Ralte & Indrajit Kar

Author:Zonunfeli Ralte & Indrajit Kar
Language: eng
Format: epub
ISBN: 9789355518972
Publisher: BPB Publications

Conclusion

Throughout the chapter, we explored various aspects of VAEs and their extensions. We began by understanding the fundamental concepts of VAEs, including the difference between VAEs and autoencoders, the network architecture, and the mathematics behind the encoder-decoder framework. We also delved into advanced techniques such as the reparameterization trick and ELBO objective function.

We then expanded our knowledge to different types of VAEs. We learned about conditional VAEs, which incorporate additional information to improve generated samples.

Additionally, we discussed the importance of understanding the latent space in VAE design. The latent space plays a crucial role in generation, interpolation, data representation, and disentanglement of factors of variation. We examined the stochastic nature of VAEs’ latent space and how it facilitates sampling and exploration.

Lastly, we touched upon challenges faced by vanilla VAEs, such as posterior collapse, blurry reconstructions, limited disentanglement, sensitivity to hyperparameters, and difficulty with large and complex datasets. Overall, our discussion provided a comprehensive understanding of VAEs and their variations, the significance of the latent space, and the challenges associated with vanilla VAEs. This knowledge equips us with a strong foundation to explore and apply VAEs in various domains, including generative modeling, data synthesis, anomaly detection, and more.

In the next chapter, we will also explore various architectural choices, such as using convolutional or recurrent networks as the encoder or decoder, to handle different types of data and capture complex dependencies. We will explore KL divergence and why it is important.

We will explore advanced techniques in VAEs. We will examine the use of different prior distributions and their impact on the generative process. We will investigate alternative forms of the encoder network, such as convolutional or recurrent networks, to handle specific data modalities effectively. Additionally, we will tackle the challenge of dealing with missing or incomplete data within the VAE framework. We will start with training a VAE on the MNIST dataset for 100 epochs and then visualize the learned latent space along with samples generated from a Dirichlet distribution compare it will normal distribution VAE. Furthermore, we will focus on Loss functions to be probable issues during training and optimization.

Join our book’s Discord space

Join the book’s Discord Workspace for Latest updates, Offers, Tech happenings around the world, New Release and Sessions with the Authors:

https://discord.bpbonline.com

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.