Unsupervised domain adaptation is an open and challenging problem in computer vision.
While existing research shows encouraging results in addressing cross-domain distribution shift on common benchmarks, they are often limited to testing under a specific target setting.
This can limit their impact for many real-world applications that present different resource constraints. In this paper, we introduce a simple yet effective framework for anytime domain adaptation that is executable with dynamic resource constraints to achieve accuracy-efficiency trade-offs under domain-shifts.
We achieve this by training a single shared network using both labeled source and unlabeled data, with switchable depth, width and input resolutions on the fly to enable testing under a wide range of computation budgets. Starting with a teacher network trained from a label-rich source domain, we utilize bootstrapped recursive knowledge distillation as a nexus between source and target domains to jointly train the student network with switchable subnetworks.
Extensive experiments on several diverse benchmark datasets well demonstrate the superiority of our proposed approach over state-of-the-art methods.
