1. A method performed by one or more computers, the method comprising: obtaining an input image; processing the input image using a deep convolutional neural network to generate an alternative representation for the input image, wherein: (i) the deep convolutional neural network includes a plurality of core neural network layers that are each defined by a respective set of parameters having current values that were determined by training a second neural network having the plurality of core neural network layers on a plurality of training images, and (ii) the second neural network was trained in part by processing, with an output layer of the second neural network and for each training image, an output of a last core neural network layer of the plurality of core neural network layers to generate, for each of a plurality of object categories, a respective score that represents a predicted likelihood that the training image contains an image of an object from the object category; and processing the alternative representation for the input image using a third neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.