Continual Source-Free Unsupervised Domain Adaptation
- URL: http://arxiv.org/abs/2304.07374v1
- Date: Fri, 14 Apr 2023 20:11:05 GMT
- Title: Continual Source-Free Unsupervised Domain Adaptation
- Authors: Waqar Ahmed, Pietro Morerio and Vittorio Murino
- Abstract summary: Existing Source-free Unsupervised Domain Adaptation approaches exhibit catastrophic forgetting.
We propose a Continual SUDA (C-SUDA) framework to cope with the challenge of SUDA in a continual learning setting.
- Score: 37.060694803551534
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing Source-free Unsupervised Domain Adaptation (SUDA) approaches
inherently exhibit catastrophic forgetting. Typically, models trained on a
labeled source domain and adapted to unlabeled target data improve performance
on the target while dropping performance on the source, which is not available
during adaptation. In this study, our goal is to cope with the challenging
problem of SUDA in a continual learning setting, i.e., adapting to the
target(s) with varying distributional shifts while maintaining performance on
the source. The proposed framework consists of two main stages: i) a SUDA model
yielding cleaner target labels -- favoring good performance on target, and ii)
a novel method for synthesizing class-conditioned source-style images by
leveraging only the source model and pseudo-labeled target data as a prior. An
extensive pool of experiments on major benchmarks, e.g., PACS, Visda-C, and
DomainNet demonstrates that the proposed Continual SUDA (C-SUDA) framework
enables preserving satisfactory performance on the source domain without
exploiting the source data at all.
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