A method of training one or more neural networks, the one or more neural networks being for use in lossy image or video encoding, transmission and decoding, the method comprising the steps of: receiving an input image at a first computer system; encoding the input image using a first neural network to produce a latent representation; decoding the latent representation using a second neural network to produce an output image, wherein the output image is an approximation of the input image; evaluating a function based on a difference between the output image and the input image; updating the parameters of the first neural network and the second neural network based on the evaluated function; and repeating the above steps using a first set of input images to produce a first trained neural network and a second trained neural network; wherein the difference between the output image and the input image is determined based on the output of a neural network acting as a discriminator; the parameters of the neural network acting as a discriminator are additionally updated based on the evaluated function; and the parameters of the neural network acting as a discriminator are updated at a first learning rate; wherein, after at least one of the updates of the parameters of the neural network acting as a discriminator, the first learning rate is updated; and the update to the first learning rate is based on an error of the output of the neural network acting as a discriminator.