The low-level and high-level features learned by a CNN on asource domain can often be transferred to augment learningin a different but related target domain. For target problemswith abundant data, we can transfer low-level features, suchas edges and corners, and learn new high-level features spe-ciﬁc to the target problem. For target problems with limitedamounts of data, learning new high-level features is difﬁcult.However, if the source and target domain are sufﬁcientlysimilar, the feature representation learned by the CNN onthe source task can also be used for the target problem. Deepfeatures extracted from CNNs trained on large annotateddatasets of images have been used as generic features very