Analysis of a Transfer Learning Application Using the Transfer Learning Test Framework  
Author Karl R. Weiss


Co-Author(s) Taghi M. Khoshgoftaar


Abstract In the field of machine learning, a transfer learning environment is when the training and testing data have different distribution properties, due to the data being drawn from different domains. A transfer learning environment is further characterized by having either very limited or no labeled test data available to build a machine learner. In a transfer learning environment, standard validation techniques (e.g. data splitting, cross-validation, etc.) may not be reliable. In previous research, a transfer learning test framework has been introduced to evaluate the robustness of algorithms to different types of distribution differences. The test framework can aid a machine learning practitioner in the evaluation of algorithms in the absence of reliable validation techniques. The transfer learning test framework stress tests machine learning algorithms in a transfer learning environment. This is accomplished by creating training and the testing data pairs that exhibit many different distribution differences that simulate conditions in a transfer learning environment. In this paper, we apply the transfer learning test framework to the source data of a transfer learning application. We then compare the test framework results to the actual results of the given transfer learning application for multiple transfer learning algorithms.


Keywords Transfer learning, Test framework analysis
    Article #:  23-151
Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design
August 3-5, 2017 - Chicago, Illinois, U.S.A.