Return of Frustratingly Easy Domain Adaptation
This addresses domain shift issues in machine learning for practical scenarios where target data is unlabeled, offering an efficient solution.
The paper tackles the problem of unsupervised domain adaptation, where target data lacks labels, by proposing CORAL, a method that aligns second-order statistics between source and target distributions, achieving strong performance on standard benchmarks with a simple implementation.
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning methods. Supervised domain adaptation methods have been proposed for the case when the target data have labels, including some that perform very well despite being "frustratingly easy" to implement. However, in practice, the target domain is often unlabeled, requiring unsupervised adaptation. We propose a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL). CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. Even though it is extraordinarily simple--it can be implemented in four lines of Matlab code--CORAL performs remarkably well in extensive evaluations on standard benchmark datasets.