The invention discloses unsupervised domain adaptation of neural networks. The methods presented herein provide unsupervised domain transfer learning. In particular, three neural networks may be trained together using at least tagged data from a first domain and untagged data from a second domain. And extracting features of the data by using a feature extraction network. A first classifier network uses the features to classify the data, while a second classifier network uses the features to determine a correlation domain. The combined loss function is used for optimizing the network, so that the feature extraction network extracts the features of the first classifier network which can be used for accurately classifying the data, but prevents the second classifier from determining the domain of the image. This optimization enables object classification to be performed with high accuracy in any domain even if the second domain may hardly have marked training data.
Unsupervised domain adaptation for neural networks