Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

In the last decade, auxiliary information has been widely used to address data sparsity. Due to the advantages of feature extraction and the no-label requirement, autoencoder-based methods addressing ...