Better Practices for Domain Adaptation
Linus Ericsson Da Li Timothy M. Hospedales
Distribution shifts are all too common in real-world applications of machine
learning. Domain adaptation (DA) aims to address this by providing various
frameworks for adapting models to the deployment data without using labels.
However, the domain shift scenario raises a second more subtle challenge: the
difficulty of performing hyperparameter optimisation (HPO) for these adaptation
algorithms without access to a labelled validation set. The unclear validation
protocol for DA has led to bad practices in the literature, such as performing
HPO using the target test labels when, in real-world scenarios, they are not
available. This has resulted in over-optimism about DA research progress
compared to reality. In this paper, we analyse the state of DA when using good
evaluation practice, by benchmarking a suite of candidate validation criteria
and using them to assess popular adaptation algorithms. We show that there are
challenges across all three branches of domain adaptation methodology including
Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and
Test Time Adaptation (TTA). While the results show that realistically
achievable performance is often worse than expected, they also show that using
proper validation splits is beneficial, as well as showing that some previously
unexplored validation metrics provide the best options to date. Altogether, our
improved practices covering data, training, validation and hyperparameter
optimisation form a new rigorous pipeline to improve benchmarking, and hence
research progress, within this important field going forward.