My questions are about underfitting/overfitting and it's related with the following results : here
In this scenario, a flat validation loss and a decreasing training loss can be considered a symptom of overfitting? I'd have expected a validation loss that starts to increase.
Moreover, at the end, the training loss was flattening, so is it correct to say that the model can't learn more with these hyperparameters? Is this, instead, a symptom of underfitting?
I'm working on this dataset (here). I implemented a convolutional neural network with 7 conv layers and 2 FC (similar to VGG, 64-P-128-128-P-256-256-P-512-512, a hidden FC of 256 neurons and the last for classifcation), clearly not to obtain a state-of-the-art score (currently about 75%).
It seems strange to me to talk about underfitting and overfitting in the same training process, so I'm pretty sure there's something I'm missing. Could you help me to understand these results?
Thanks for your attention
I've found a similar question but it didn't help (here).