Transferable Meta Learning Across Domains

Meta learning algorithms are effective at ob- taining meta models with the capability of solving new tasks quickly. However, they crit- ically require sufficient tasks for meta model training and the resulted model can only solve new tasks similar to the training ones. These limitations make them suffer performance de- cline in presence of insufficiency of training tasks in target domains and task heterogene- ity—the source (model training) tasks presents different characteristics from target (model ap- plication) tasks. To overcome these two signif- icant limitations of existing meta learning al- gorithms, we introduce the cross-domain meta learning framework and propose a new trans- ferable meta learning (TML) algorithm. TML performs meta task adaptation jointly with meta model learning, which effectively nar- rows divergence between source and target tasks and enables transferring source meta- knowledge to solve target tasks. Thus, the re- sulted transferable meta model can solve new learning tasks in new domains quickly. We ap- ply the proposed TML to cross-domain few- shot classification problems and evaluate its performance on multiple benchmarks. It per- forms significantly better and faster than well- established meta learning algorithms and fine- tuned domain-adapted models.