I trained a neural network model, a MLP type of network, where the first several layers are 1-D convolution for processing sequence type of input.
However, the training process looks like as follows, where the orange line represents the validation loss and the blue line represents the training loss. The validation loss is large compared to the training loss and the training loss also stops decreasing after the first several iterations. Are there any generic guidance to improve the performance? I have about 1 million training traces, and the number of parameters of the network is about 140K.