How should the Specialist address this issue and what is the reason behind it?
The learning rate should be increased because the optimization process was trapped at a local minimum.
The dropout rate at the flatten layer should be increased because the model is not generalized enough.
The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
Explanations:
Increasing the learning rate is not a direct solution for overfitting. Overfitting typically results from the model being too complex relative to the data, not because of optimization being trapped at a local minimum.
Increasing the dropout rate can help prevent overfitting by randomly setting some neurons to zero during training, forcing the model to generalize better rather than memorizing the training data.
Increasing the dimensionality of the dense layer would likely increase the complexity of the model, which can exacerbate overfitting, not reduce it.
Overfitting is not caused by premature termination of training. In fact, increasing the epoch number could exacerbate overfitting by allowing the model to memorize more details of the training data.
I suspect that the answer is:
The dropout rate at the flatten layer should be increased because the model is not generalized enough.