Certainty and Uncertainty Guided Active Domain Adaptation
This work addresses domain adaptation for machine learning models by improving sample selection efficiency, though it is incremental as it builds on existing ADA methods.
The paper tackles the problem of Active Domain Adaptation (ADA) by proposing a collaborative framework that labels uncertain samples while treating highly confident predictions as ground truth, resulting in outperforming state-of-the-art ADA methods on Office-Home and DomainNet datasets.
Active Domain Adaptation (ADA) adapts models to target domains by selectively labeling a few target samples. Existing ADA methods prioritize uncertain samples but overlook confident ones, which often match ground-truth. We find that incorporating confident predictions into the labeled set before active sampling reduces the search space and improves adaptation. To address this, we propose a collaborative framework that labels uncertain samples while treating highly confident predictions as ground truth. Our method combines Gaussian Process-based Active Sampling (GPAS) for identifying uncertain samples and Pseudo-Label-based Certain Sampling (PLCS) for confident ones, progressively enhancing adaptation. PLCS refines the search space, and GPAS reduces the domain gap, boosting the proportion of confident samples. Extensive experiments on Office-Home and DomainNet show that our approach outperforms state-of-the-art ADA methods.