A signal processing system for image generation, wherein the signal processing system is configured to generate an image conditional on a context defined by a context vector (c), wherein said context vector defines a relative likelihood of each of a plurality of categories of images, and wherein the signal processing system is configured to generate an image from a selected category, the signal processing system comprising: a probability vector generation system ( B; 108), wherein said probability vector generation system has an input to receive a category vector ( m k) for a category (k) of image output example, and an output to provide a probability vector ( p v) for said category of image output example, wherein said category vector comprises a compressed representation of said probability vector, wherein said image output example comprises a set of data points, and wherein said probability vector defines a probability of each of said set of data points for said category of image output example, learned from training example images; a memory storing a plurality of said category vectors (106; m), one for each of a plurality of said categories of image output example, learned from the training example images; and a stochastic selector to select a said stored category of image output example (x) for presentation of the corresponding category vector to said probability vector generation system; wherein said signal processing system is configured to output data comprising an image for an image output example corresponding to said selected stored category, wherein said probability vector generation system comprises a deterministic neural network, and wherein said signal processing system further comprises an output stochastic selector (110; x∼p v) having an input coupled to receive said probability vector from said probability vector generation system and configured to generate and output said data for an image output example; a context vector input to receive a context vector ( c), wherein said context vector defines a relative likelihood of each of said plurality of said categories; wherein said context vector input is coupled to said stochastic selector such that said selection of said category of image output example is dependent on said context vector ( p ( k | c)). a probability vector generation system ( B; 108), wherein said probability vector generation system has an input to receive a category vector ( m k) for a category (k) of image output example, and an output to provide a probability vector ( p v) for said category of image output example, wherein said category vector comprises a compressed representation of said probability vector, wherein said image output example comprises a set of data points, and wherein said probability vector defines a probability of each of said set of data points for said category of image output example, learned from training example images; a memory storing a plurality of said category vectors (106; m), one for each of a plurality of said categories of image output example, learned from the training example images; and a stochastic selector to select a said stored category of image output example (x) for presentation of the corresponding category vector to said probability vector generation system; wherein said signal processing system is configured to output data comprising an image for an image output example corresponding to said selected stored category, wherein said probability vector generation system comprises a deterministic neural network, and wherein said signal processing system further comprises an output stochastic selector (110; x∼p v) having an input coupled to receive said probability vector from said probability vector generation system and configured to generate and output said data for an image output example; a context vector input to receive a context vector ( c), wherein said context vector defines a relative likelihood of each of said plurality of said categories; wherein said context vector input is coupled to said stochastic selector such that said selection of said category of image output example is dependent on said context vector ( p ( k | c)).