Abstract: A computer-implemented method for generating a training dataset. The method comprises receiving content comprising one or more elements, generating an element representation for each of the one or more elements by processing the content and one or more user-generated primitive element representations, generating a synthetic user-generated representation of the one or more elements based upon the one or more user-generated primitive element representations and the layout of the one or more elements in the content, and generating the training dataset based upon the synthetic user-generated representation and the content.

Abstract: HET classifiers, which learn a multivariate Gaussian distribution over prediction logits, perform well on image classification problems with hundreds to thousands of classes. However, compared to standard classifiers (e.g., deterministic (DET) classifiers), they introduce extra parameters that scale linearly with the number of classes. This makes them infeasible to apply to larger-scale problems. In addition, HET classifiers introduce a temperature hyperparameter, which is ordinarily tuned. HET classifiers are disclosed, where the parameter count (when compared to a DET classifier) scales independently of the number of classes. In large-scale settings of the embodiments, the need to tune the temperature hyperparameter is removed, by directly learning it on the training data.