Uncertainty-guided Source-free Domain Adaptation

by   Subhankar Roy, et al.

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation. For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions. Uncertainties are estimated by employing a Laplace approximation and incorporated to identify target data points that do not lie in the source manifold and to down-weight them when maximizing the mutual information on the target data. Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source training or changes of the model architecture. We show the advantages of uncertainty-guided SFDA over traditional SFDA in the closed-set and open-set settings and provide empirical evidence that our approach is more robust to strong domain shifts even without tuning.


page 1

page 2

page 3

page 4


On-target Adaptation

Domain adaptation seeks to mitigate the shift between training on the so...

One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift

In this paper, we investigate open-set recognition with domain shift, wh...

Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data

Present domain adaptation methods usually perform explicit representatio...

DistillAdapt: Source-Free Active Visual Domain Adaptation

We present a novel method, DistillAdapt, for the challenging problem of ...

Approximating a Target Distribution using Weight Queries

A basic assumption in classical learning and estimation is the availabil...

Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics

In this paper, we propose a novel domain adaptation method for the sourc...

Active Source Free Domain Adaptation

Source free domain adaptation (SFDA) aims to transfer a trained source m...