Discriminative and Generative Models Compared

Understand the differences between discriminative and generative models.

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Comparing discriminative and generative models

Broadly speaking, machine learning models can be subdivided into discriminative models and generative models. Discriminative models learn a map from some input to some output. In discriminative models, learning the process that generates the input is not relevant; it will just learn a map from the input to the expected output.

Generative models, on the other hand, in addition to learning a map from some input to some output, also learn the process that generates the input and the output. Refer to the following imageSource: Ian Goodfellow's Tutorial on Generative Adversarial Networks, 2017:

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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

Discriminative and Generative Models Compared