  Unsupervised domain adaptation (UDA) aims to transfer knowledge from a
labeled source domain to an unlabeled target domain. Owing to privacy concerns
and heavy data transmission, source-free UDA, exploiting the pre-trained source
models instead of the raw source data for target learning, has been gaining
popularity in recent years. Some works attempt to recover unseen source domains
with generative models, however introducing additional network parameters.
Other works propose to fine-tune the source model by pseudo labels, while noisy
pseudo labels may misguide the decision boundary, leading to unsatisfied
results. To tackle these issues, we propose an effective method named
Proxy-based Mixup training with label refinery (ProxyMix). First of all, to
avoid additional parameters and explore the information in the source model,
ProxyMix defines the weights of the classifier as the class prototypes and then
constructs a class-balanced proxy source domain by the nearest neighbors of the
prototypes to bridge the unseen source domain and the target domain. To improve
the reliability of pseudo labels, we further propose the frequency-weighted
aggregation strategy to generate soft pseudo labels for unlabeled target data.
The proposed strategy exploits the internal structure of target features, pulls
target features to their semantic neighbors, and increases the weights of
low-frequency classes samples during gradient updating. With the proxy domain
and the reliable pseudo labels, we employ two kinds of mixup regularization,
i.e., inter- and intra-domain mixup, in our framework, to align the proxy and
the target domain, enforcing the consistency of predictions, thereby further
mitigating the negative impacts of noisy labels. Experiments on three 2D image
and one 3D point cloud object recognition benchmarks demonstrate that ProxyMix
yields state-of-the-art performance for source-free UDA tasks.
