Dear author,
I really appreciate your codes for implementing the ASSERT model. It is an excellent work!!!
But in practice, I got some questions. I found that when I ran the scripts multiple times (with exact same settings), the results can be much different and had a large range. For e.g., I ran 10 times the SE-ResNet34 model for replay detetion task with your default settings, the best performance could archieve an EER of 0.67% for dev and EER of 1.11% for eval, but the worst one has an EER of 1.50% for dev and EER of 2.02% for eval. I am curious about the reason behind this. Does the model parameters initialization have such great impact? Or other reasons? How can I avoid this and make the training more stable? Could you please give some suggestions on it?
Thanks a lot!!!
Dear author,
I really appreciate your codes for implementing the ASSERT model. It is an excellent work!!!
But in practice, I got some questions. I found that when I ran the scripts multiple times (with exact same settings), the results can be much different and had a large range. For e.g., I ran 10 times the SE-ResNet34 model for replay detetion task with your default settings, the best performance could archieve an EER of 0.67% for dev and EER of 1.11% for eval, but the worst one has an EER of 1.50% for dev and EER of 2.02% for eval. I am curious about the reason behind this. Does the model parameters initialization have such great impact? Or other reasons? How can I avoid this and make the training more stable? Could you please give some suggestions on it?
Thanks a lot!!!