A method of generating a training data set for training a classifier comprising: • providing a first set of multi-modal training samples comprising first visual data samples and first textual data samples of a number of base classes, • providing a second set of multi-modal training samples comprising second visual data samples and second textual data samples of a number of novel classes, the number of training samples per base class in the first set of multi-modal training samples being greater than the number of training samples per novel class in the second set of multi-modal training samples, • text conditioned training a generative model G using the first set of multi-modal training samples, the training of the generative model G comprising: providing a discriminative model, training the generative model and the discriminative model using the first set of multi-modal training samples, the training comprising: training the generative model to generate visual data samples from the first textual samples and training the discriminative model, using generated visual data samples by the generative model and the first visual data samples, to determine if a visual data sample is a first visual data sample of the first set or a generated visual data sample, wherein the training is performed with backpropagation for optimizing in an adversarial way a loss function of the generative model and another loss function of the discriminative model; • using the trained generative model G to transform the second textual data samples into output visual data samples of the novel classes, the training data set comprising at least part of the output visual data samples, and characterized in that the method further comprises: • ranking the output visual data samples in accordance with an image quality of the output visual data samples, wherein a discriminative model's score of the output visual data sample is indicative of the image quality of the output visual data sample, selecting a subsample of the output visual data samples based on the ranking, the training set comprising the selected subsample. • providing a first set of multi-modal training samples comprising first visual data samples and first textual data samples of a number of base classes, • providing a second set of multi-modal training samples comprising second visual data samples and second textual data samples of a number of novel classes, the number of training samples per base class in the first set of multi-modal training samples being greater than the number of training samples per novel class in the second set of multi-modal training samples, • text conditioned training a generative model G using the first set of multi-modal training samples, the training of the generative model G comprising: providing a discriminative model, training the generative model and the discriminative model using the first set of multi-modal training samples, the training comprising: training the generative model to generate visual data samples from the first textual samples and training the discriminative model, using generated visual data samples by the generative model and the first visual data samples, to determine if a visual data sample is a first visual data sample of the first set or a generated visual data sample, wherein the training is performed with backpropagation for optimizing in an adversarial way a loss function of the generative model and another loss function of the discriminative model; • using the trained generative model G to transform the second textual data samples into output visual data samples of the novel classes, the training data set comprising at least part of the output visual data samples, • ranking the output visual data samples in accordance with an image quality of the output visual data samples, wherein a discriminative model's score of the output visual data sample is indicative of the image quality of the output visual data sample, selecting a subsample of the output visual data samples based on the ranking, the training set comprising the selected subsample.