One of the primary challenges in Semi-supervised Domain Adaptation (SSDA) is
the skewed ratio between the number of labeled source and target samples,
causing the model to be biased towards the source domain. Recent works in SSDA
show that aligning only the labeled target samples with the source samples
potentially leads to incomplete domain alignment of the target domain to the
source domain. In our approach, to align the two domains, we leverage
contrastive losses to learn a semantically meaningful and a domain agnostic
feature space using the supervised samples from both domains. To mitigate
challenges caused by the skewed label ratio, we pseudo-label the unlabeled
target samples by comparing their feature representation to those of the
labeled samples from both the source and target domains. Furthermore, to
increase the support of the target domain, these potentially noisy
pseudo-labels are gradually injected into the labeled target dataset over the
course of training. Specifically, we use a temperature scaled cosine similarity
measure to assign a soft pseudo-label to the unlabeled target samples.
Additionally, we compute an exponential moving average of the soft
pseudo-labels for each unlabeled sample. These pseudo-labels are progressively
injected or removed) into the (from) the labeled target dataset based on a
confidence threshold to supplement the alignment of the source and target
distributions. Finally, we use a supervised contrastive loss on the labeled and
pseudo-labeled datasets to align the source and target distributions. Using our
proposed approach, we showcase state-of-the-art performance on SSDA benchmarks
- Office-Home, DomainNet and Office-31.

本研究通过引入对比损失和伪标记机制，对半监督域自适应问题进行了有效解决，并在 Office-Home、DomainNet 和 Office-31 数据集上取得了最先进的性能。