Using pseudo-labels for SFDA can have limitations such as:
Label Noise: Pseudo-labels generated through clustering methods may contain noise, leading to inaccuracies in the adaptation process. This can result in suboptimal performance and hinder the model's ability to generalize to the target domain.
Domain Shift: Pseudo-labels may not fully capture the underlying distribution shift between the source and target domains, leading to mismatched adaptations. This can impact the model's ability to effectively adapt to the target domain.
Scalability: Generating pseudo-labels for large datasets can be computationally expensive and time-consuming, especially in the context of medical imaging datasets with high-resolution images.
To address these limitations more effectively, techniques such as:
Semi-Supervised Learning: Incorporating a small amount of labeled data from the target domain along with pseudo-labels can help in improving the quality of the labels and enhancing the adaptation process.
Active Learning: Implementing active learning strategies to iteratively select the most informative samples for pseudo-labeling can help in reducing label noise and improving the overall quality of the pseudo-labels.
Ensemble Methods: Utilizing ensemble methods to combine multiple pseudo-labeling strategies can help in mitigating the impact of label noise and improving the robustness of the adaptation process.