Prevent model from over-focusing on strong features
Use dropout.
Dropout is a form of regularization where you zero out the input components with a certain probability.
If you want your model to learn representations that rely less on some features, just place a dropout layer before those features enter the rest of the network. This will force the network to learn more robust representations that depend less on the strongest features.