Overfitting when training ACDNet20 in TensorFlow

[mrerdem]

Thank you for the paper and the repository.

When I try to follow the procedure for "B. Rebuilding ACDNet20 in Tensorflow" the model starts overfitting after just several epochs and final validation accuracy does not go beyond ~30%. I am using "micro_acdnet_pruned_trained_fold4_86.00" as the reference torch model in the first input.

I should also note that the structure of pretrained tf model (acdnet20_20khz_fold4) does not match any of the 4 torch models provided. The pre-trained TF model has conv filters of [4, 32, 12, 23, 18, 38, 43, 62, 58, 77, 37, 50] whereas torch model has [7, 20, 10, 14, 22, 31, 35, 41, 51, 67, 69, 48]. Anyway, I also tried using the architecture of the pre-trained TF model to train in TF from scratch, but overfitting is still there.

At first I thought it is because weight_decay parameter in SGD is now deprecated. However, I implemented L2 regularization for the same functionality in all Conv layers, but overfitting is still there.

Do you have any suggestion to fix this issue?