Include regularization term: By adding a regularization term to the loss function, the complexity of the model can be limited to prevent overfitting. Common regularization methods include L1 regularization and L2 regularization.
Stopping training early: Monitor the performance of the validation set during training and stop training early when the performance starts to decline, in order to prevent overfitting of the model.
Data augmentation helps reduce the risk of model overfitting by increasing the diversity of training data. Common methods of data augmentation include random rotation, cropping, and translation.
Dropout: randomly turning off a portion of neurons during training can effectively reduce the risk of model overfitting.
Ensemble learning: By combining multiple different models, the risk of overfitting can be reduced. Common ensemble learning methods include Bagging and Boosting.
Decreasing model complexity: If the model is too complex, consider reducing the number of layers or hidden units to lower the model complexity and prevent overfitting.
