The Frechet Inception Distance or FID is a method for comparing the statistics of two distributions by computing the distance between them. In GANs, the FID method is used for computing how much the distribution of the Generator looks like the distribution of the Discriminator. By consequence, it is a metric of GAN performance – the lower the FID, the better the GAN.
It is named Inception Distance because you’re using an Inception neural network (say, InceptionV3) for computing the distance. Here’s how you’ll do that, technically:
Learn how large language models and other foundation models are working and how you can train open source ones yourself.
Keras is a high-level API for TensorFlow. It is one of the most popular deep learning frameworks.
Read about the fundamentals of machine learning, deep learning and artificial intelligence.
To get in touch with me, please connect with me on LinkedIn. Make sure to write me a message saying hi!
The content on this website is written for educational purposes. In writing the articles, I have attempted to be as correct and precise as possible. Should you find any errors, please let me know by creating an issue or pull request in this GitHub repository.
All text on this website written by me is copyrighted and may not be used without prior permission. Creating citations using content from this website is allowed if a reference is added, including an URL reference to the referenced article.
If you have any questions or remarks, feel free to get in touch.
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
PyTorch, the PyTorch logo and any related marks are trademarks of The Linux Foundation.
Montserrat and Source Sans are fonts licensed under the SIL Open Font License version 1.1.
Mathjax is licensed under the Apache License, Version 2.0.