How to Implement the Inception Score (IS) for Evaluating GANs
Last Updated on October 11, 2019
Generative Adversarial Networks, or GANs for short, is a deep learning neural network architecture for training a generator model for generating synthetic images.
A problem with generative models is that there is no objective way to evaluate the quality of the generated images.
As such, it is common to periodically generate and save images during the model training process and use subjective human evaluation of the generated images in order to both evaluate the quality of the generated images and to select a final generator model.
Many attempts have been made to establish an objective measure of generated image quality. An early and somewhat widely adopted example of an objective evaluation method for generated images is the Inception Score, or IS.
In this tutorial, you will discover the inception score for evaluating the quality of generated images.
After completing this tutorial, you will know:
- How to calculate the inception score and the intuition behind what it measures.
- How to implement the inception score in Python with NumPy and the Keras deep learning library.
- How to calculate the inception score for small images such as those in the CIFAR-10 dataset.
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