Generative Adversarial Networks<\/a>, or GANs for short, is a deep learning neural network architecture for training a generator model for generating synthetic images.<\/p>\n A problem with generative models is that there is no objective way to evaluate the quality of the generated images.<\/p>\n 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.<\/p>\n 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.<\/p>\n In this tutorial, you will discover the inception score for evaluating the quality of generated images.<\/p>\n After completing this tutorial, you will know:<\/p>\n Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book<\/a>, with 29 step-by-step tutorials and full source code.<\/p>\n Let\u2019s get started.<\/p>\n How to Implement the Inception Score (IS) From Scratch for Evaluating Generated Images This tutorial is divided into five parts; they are:<\/p>\n The Inception Score, or IS for short, is an objective metric for evaluating the quality of generated images, specifically synthetic images output by generative adversarial network models.<\/p>\n The inception score was proposed by Tim Salimans<\/a>, et al. in their 2016 paper titled \u201cImproved Techniques for Training GANs<\/a>.\u201d<\/p>\n In the paper, the authors use a crowd-sourcing platform (Amazon Mechanical Turk<\/a>) to evaluate a large number of GAN generated images. They developed the inception score as an attempt to remove the subjective human evaluation of images.<\/p>\n The authors discover that their scores correlated well with the subjective evaluation.<\/p>\n As an alternative to human annotators, we propose an automatic method to evaluate samples, which we find to correlate well with human evaluation \u2026<\/p>\n<\/blockquote>\n \u2014 Improved Techniques for Training GANs<\/a>, 2016.<\/p>\n The inception score involves using a pre-trained deep learning neural network model for image classification to classify the generated images. Specifically, the Inception v3 model<\/a> described by Christian Szegedy<\/a>, et al. in their 2015 paper titled \u201cRethinking the Inception Architecture for Computer Vision<\/a>.\u201d The reliance on the inception model gives the inception score its name.<\/p>\n A large number of generated images are classified using the model. Specifically, the probability of the image belonging to each class is predicted. These predictions are then summarized into the inception score.<\/p>\n The score seeks to capture two properties of a collection of generated images:<\/p>\n The inception score has a lowest value of 1.0 and a highest value of the number of classes supported by the classification model; in this case, the Inception v3 model supports the 1,000 classes of the ILSVRC 2012 dataset<\/a>, and as such, the highest inception score on this dataset is 1,000.<\/p>\n\n
![\"How](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2019\/08\/How-to-Implement-the-Inception-Score-IS-From-Scratch-for-Evaluating-Generated-Images.jpg\")
<\/p>\n
Photo by alfredo affatato<\/a>, some rights reserved.<\/p>\n<\/div>\nTutorial Overview<\/h2>\n
\n
What Is the Inception Score?<\/h2>\n
\n
\n