Disentangling Transfer and Interference in Multi-Domain Learning

Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks. Likewise, transfer learning has enabled enormous success in many computer vision problems using pretraining. However, the benefits of transfer in multi-domain learning, where a network learns multiple tasks defined by different datasets, has not been adequately studied. Learning multiple domains could be beneficial or these domains could interfere with each other given limited network capacity. In this work, we decipher the conditions where interference and knowledge transfer occur in multi-domain learning. We propose new metrics disentangling interference and transfer and set up experimental protocols. We further examine the roles of network capacity, task grouping, and dynamic loss weighting in reducing interference and facilitating transfer. We demonstrate our findings on the CIFAR-100, MiniPlaces, and Tiny-ImageNet datasets.