A learning rate scheduler reduced the learning rate by 0.5× after 30 epochs without improvement in the validation loss, with a minimum learning rate of 10 -7 . Early stopping was applied after 50 epochs without improvement in the validation loss, and training was capped at 1000 epochs. Initial learning rates of 10 -3 , 10 -4 , and 10 -5 , and per-GPU batch sizes of 4, 8, 16, 32, and 64 were used during hyperparameter optimization experiments, while keeping the network architecture hyperparameters fixed. Models were trained for 50 epochs during hyperparameter optimization to approximate a full training budget, with a limited percentage of the total training budget used to calculate TSE.