One strategy to overcome overfitting is to use regularization techniques such as L1 or L2 regularization. These techniques add a penalty term to the loss function, helping to prevent the model from fitting the noise in the training data. Another strategy is to use cross-validation to get a better estimate of the model's performance on unseen data. This helps in identifying whether the model is overfitting or not. Additionally, collecting more data or applying data augmentation techniques can also help reduce overfitting by providing the model with a more diverse and representative dataset.

Aside from the mentioned strategies, one could also consider applying dimensionality reduction techniques like Principle Component Analysis (PCA) or feature selection methods to reduce the complexity of the dataset. Another option is to incorporate early stopping with a decayed learning rate, which gradually decreases the learning rate as training progresses. This helps the model to make smaller updates as it converges, preventing it from overfitting. Another interesting approach is using generative adversarial networks (GANs) to augment the training data by generating synthetic samples that closely resemble the original data distribution, thus increasing its diversity.

In addition to regularization and cross-validation, another approach to mitigate overfitting is early stopping. This involves monitoring the model's performance on a validation set during training and stopping the training process when the performance starts to degrade. This prevents the model from over-optimizing the training data and improves its ability to generalize to unseen data. Another technique is dropout, which randomly drops some neurons during training, forcing the model to learn more robust and generalized representations. Also, ensemble methods like bagging or boosting can be effective in reducing overfitting by combining multiple models.