The complementary information from various Parameter-Efficient Transfer Learning (PETL) approaches can be effectively utilized in several advanced learning paradigms beyond traditional ensemble methods. In semi-supervised learning, for instance, the diverse predictions generated by different PETL methods can be harnessed to create pseudo-labels for unlabeled data. By leveraging the unique inductive biases of each PETL approach, one can generate a richer set of pseudo-labels that capture different aspects of the data distribution. This can enhance the model's robustness and generalization capabilities, as it learns from a more varied set of labeled examples.
In the context of domain adaptation, the insights gained from the different PETL methods can be used to design adaptive mechanisms that selectively apply the most relevant features learned from the source domain to the target domain. For example, one could implement a strategy where the model dynamically adjusts the weights of different PETL methods based on the similarity of the target domain to the source domain. This would allow the model to retain the beneficial knowledge from the pre-trained model while adapting to the new domain's specific characteristics, thus improving performance in scenarios where domain shifts are significant.