Heterogeneous Few-Shot Model Rectification With Semantic Mapping

There still involve lots of challenges when applying machine learning algorithms in
unknown
environments, especially those with
limited
training data. To handle the data insufficiency and make a further step towards robust learning, we adopt the
learnware
notion Z.-H. Zhou, “Learnware: On the future of machine learning,”
Front. Comput. Sci.
, vol. 10, no. 4 pp. 589–590, 2016 which equips a model with an essential
reusable
property—the model learned in a related task could be easily adapted to the current data-scarce environment
without data sharing
. To this end, we propose the REctiFy via heterOgeneous pRedictor Mapping (
ReForm
) framework enabling the current model to take advantage of a related model from two kinds of
heterogeneous environment
, i.e., either with different sets of features or labels. By Encoding Meta InformaTion (
Emit
) of features and labels as the model specification, we utilize an optimal transported semantic mapping to characterize and bridge the environment changes. After fine-tuning over a few labeled examples through a biased regularization objective, the transformed heterogeneous model adapts to the current task efficiently. We apply
ReForm
over both synthetic and real-world tasks such as few-shot image classification with either learned or pre-defined specifications. Experimental results validate the effectiveness and practical utility of the proposed
ReForm
framework.