1. A method performed by one or more computers, the method comprising: obtaining training data for a neural network, wherein the training data comprises a plurality of base training images and respective classification data for each of the base training images, and wherein the neural network is configured to receive an input image and predict classification data for the input image, wherein each image comprises data representing pixels having a respective color; generating one or more color-deformed images from the base training images of the training data, the generating comprising, for each of the plurality of base training images: performing a principal component analysis (PCA) on pixels in a first region of the base training image to obtain a plurality of eigenvector-eigenvalue pairs of a covariance matrix of red green blue (RGB) pixel values from the pixels in the first region of the base training image; and applying an intensity transformation of pixel colors of the pixels in the first region of the base training image, comprising: randomly selecting a respective value for each eigenvector-eigenvalue pair of the covariance matrix; and for each pixel in the first region of the base training image, applying a transformation to the pixel colors of the pixel based on the eigenvector-eigenvalue pairs and the randomly-selected values; and adding the one or more color-deformed images to the training data for the neural network.