One potential limitation of the self-distillation loss approach is the risk of overfitting, especially when the model is trained on limited data or when the loss function is not properly regularized. To address this, regularization techniques such as dropout, weight decay, or data augmentation can be employed to prevent overfitting and improve the generalization of the model.
Another drawback could be the sensitivity of the self-distillation loss to the choice of hyperparameters, such as the weighting factor λ. Suboptimal values of λ may lead to subpar performance or convergence issues. To mitigate this, a systematic hyperparameter search or optimization process can be conducted to find the optimal value of λ that maximizes the performance of the model.
Additionally, the self-distillation loss approach may introduce additional computational overhead, especially during the optimization process. This can impact training efficiency and scalability, particularly when dealing with large-scale datasets. Implementing efficient optimization algorithms and parallel processing techniques can help mitigate the computational burden and improve the overall efficiency of the self-distillation approach.