Importing CIFAR

Deep neural networks are a powerful tool for creating generative models for complex data such as images, allowing us to develop a network that can generate images from the MNIST hand-drawn digit database. In that example, the data is relatively simple; images can only come from a limited set of categories (the digits 0 through 9) and are low-resolution grayscale data.

What about more complex data, such as color images drawn from the real world? One example of such “real world” data is the Canadian Institute for Advanced Research 10 class dataset, denoted as CIFAR-10. It is a subset of 60,000 examples from a larger set of 80 million images, divided into ten classes—airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. While still an extremely limited set in terms of the diversity of images we would encounter in the real world, these classes have some characteristics that make them more complex than MNIST. For example, the MNIST digits can vary in width, curvature, and a few other properties; the CIFAR-10 classes have a much wider potential range of variation for animal or vehicle photos, meaning we may require more complex models to capture this variation.