Uncertainty-guided Source-free Domain Adaptation
- URL: http://arxiv.org/abs/2208.07591v1
- Date: Tue, 16 Aug 2022 08:03:30 GMT
- Title: Uncertainty-guided Source-free Domain Adaptation
- Authors: Subhankar Roy, Martin Trapp, Andrea Pilzer, Juho Kannala, Nicu Sebe,
Elisa Ricci, Arno Solin
- Abstract summary: Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.
We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
- Score: 77.3844160723014
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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