Transfer learning is an approach to extract features from source domain and transfer the knowledge of source domain to the target domain, so as to improve the performance of target learners while relieving the pressure to collect a lot of target-domain data. It has been put into wide applications, such as image classification and text classification.
Domain adaptation on digital number datasets, namely MNIST, USPS and SVHN, is the task of transferring learning models across different data sets, aiming to conduct cross-domain feature alignment and build a generalized model that is able to predict labels among various digital number datasets.
Presently, we have trained a green-learning based transfer learning model between MNIST and USPS. The first step is preprocessing and feature extraction, including feature processing for different dataset in order to make them visually similar, raw Saab feature extraction  and LNT feature transformation  followed by cosine similarity check to find discriminant features. The second step is joint subspace generation, in which for each label in source domain, k-means with number of clusters as 1, 2, 4, 8 are performed separately in order to generate 10, 20, 40 and 80 subspaces, then assign target features to generated subspaces. The third step is to utilize assigned datas to conduct weakly supervised learning to predict label
for rest target data samples. Our goal is to compare and analyze the performance of our green-learning based transfer learning models with other models. In future, we aim to conduct transfer learning among these three digital number datasets mutually and improve the accuracy by improving cross-domain feature alignment.
 Zhuang, Fuzhen, et al. “A comprehensive survey on transfer learning.” Proceedings of the IEEE 109.1 (2020): 43-76.
 Y. Chen, M. Rouhsedaghat, [...]