1. A method of training a convolutional neural network (CNN) for domain adaptation utilizing features extracted from multiple levels, including: selecting a CNN architecture including a plurality of convolutional layers and fully connected layers; training the CNN on a source domain data set; selecting a plurality of layers from the plurality of convolutional layers across the trained CNN; extracting features from the selected layers from the trained CNN; concatenating the extracted features to form a feature vector; connecting the feature vector to a fully connected neural network classifier; and, fine-tuning the fully connected neural network classifier from a target domain data set by optimizing weights of the CNN with respect to the target domain data set by more strongly optimizing weights of higher network layers of the CNN compared with lower network layers of the CNN.