The insights gained from the study on PETL's ability to preserve pre-trained knowledge can be highly beneficial in other transfer learning scenarios, such as continual learning and multi-task learning. In continual learning, where models are required to learn from a stream of tasks without forgetting previously acquired knowledge, the ability of PETL methods to maintain the robustness of pre-trained models can be leveraged to mitigate catastrophic forgetting. By selectively tuning only a small number of parameters, PETL approaches can help retain essential features learned from earlier tasks while allowing the model to adapt to new tasks.
In multi-task learning, the complementary information from different PETL methods can be utilized to enhance the model's performance across various tasks. By applying different PETL approaches to different tasks, one can exploit the unique strengths of each method, allowing the model to learn shared representations that are beneficial across tasks while still being able to adapt to task-specific requirements. This can lead to improved generalization and performance, as the model can draw on a diverse set of learned features that are relevant to multiple tasks.
Overall, the ability of PETL methods to balance the retention of pre-trained knowledge with the flexibility to adapt to new tasks makes them a valuable tool in the broader context of transfer learning, enabling more effective learning strategies in both continual and multi-task scenarios.