Generative Models

Explore autoregressive models, variational autoencoders (VAEs), and reversible flow models for generative tasks.

We'll cover the following

Autoregressive models
Variational autoencoders (VAEs)
Reversible flows

We will look into the various approaches of generative models.

Autoregressive models

An autoregressive model estimates the conditional distribution of some data $y$ , given some other values of $y$ . For example, in image synthesis, it estimates the conditional distribution of pixels given surrounding or previous pixels; in audio synthesis, it estimates the conditional distribution of audio samples given previous audio samples and spectrograms.

In its simplest linear form, with dependency on the previous time-step only and time-invariant bias term, an autoregressive model can be defined with the following equation:

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Getting Started

Deep Learning Basics and Environment Test

Introduction to Generative Models

Implementing Our First GAN

Evaluating Our First GAN

Improving Our First GAN

Synthesizing and Manipulating Images with GANs

Progressive Growing of GANs

Generation of Discrete Sequences Using GANs

Text-to-Image Synthesis with GANs

Speech Enhancement with GANs

TequilaGAN—Identifying GAN Samples

What’s Next in GANs

Conclusion

Appendix

Generative Models

Autoregressive models