Skip to yearly menu bar Skip to main content

In-Person Poster presentation / poster accept

AnyDA: Anytime Domain Adaptation

Omprakash Chakraborty · Aadarsh Sahoo · Rameswar Panda · Abir Das

MH1-2-3-4 #29

Keywords: [ Applications ] [ knowledge distillation ] [ Resource-constrained Learning ] [ Anytime Prediction ] [ Efficient Domain Adaptation ]


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 constrained to testing under a specific target setting, limiting their impact for many real-world applications. 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. Experiments on multiple datasets well demonstrate the superiority of our approach over state-of-the-art methods.

Chat is not available.