One potential limitation of the dynamic threshold adjustment approach is the complexity of the meta-learning process and the computational resources required for training the online threshold generator. As the online threshold generator needs to continuously adapt the thresholds based on the training progress, it may introduce additional computational overhead and training time.
To address this limitation, future work can focus on optimizing the meta-learning algorithm used in the online threshold generator to make it more efficient and less resource-intensive. This can involve exploring more lightweight meta-learning techniques, such as model-agnostic meta-learning (MAML) or gradient-based meta-learning, to reduce the computational burden while still achieving accurate threshold estimation.
Additionally, incorporating techniques like transfer learning or pre-training the online threshold generator on related tasks can help accelerate the convergence of the threshold adjustment process and improve its overall efficiency. By streamlining the meta-learning process and optimizing the online threshold generator, the potential limitations of the dynamic threshold adjustment approach can be mitigated.