Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
Julio Ivan Davila Carrazco Pietro Morerio Alessio Del Bue Vittorio Murino
This paper presents a classification framework based on learnable data
augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA)
problem. OS-UDA is the most challenging setting in Domain Adaptation, as only
one single unlabeled target sample is assumed to be available for model
adaptation. Driven by such single sample, our method LearnAug-UDA learns how to
augment source data, making it perceptually similar to the target. As a result,
a classifier trained on such augmented data will generalize well for the target
domain. To achieve this, we designed an encoder-decoder architecture that
exploits a perceptual loss and style transfer strategies to augment the source
data. Our method achieves state-of-the-art performance on two well-known Domain
Adaptation benchmarks, DomainNet and VisDA. The project code is available at
https://github.com/IIT-PAVIS/LearnAug-UDA