Improve

How to improve deep learning model?
Improve - Troubleshooting

Summary

  • The first step is to address under-fitting:

    • Add model complexity → Reduce regularization → Error analysis → Choose a more complex architecture → Tune hyper-parameters → Add features.

  • The second step is to address over-fitting:

    • Add more training data → Add normalization → Add data augmentation → Increase regularization → Error analysis → Choose a more complex architecture → Tune hyper-parameters → Early stopping → Remove features → Reduce model size.

  • The third step is to address the distribution shift present in the data:

    • Analyze test-validation errors and collect more training data to compensate.

    • Analyze test-validation errors and synthesize more training data to compensate.

    • Apply domain adaptation techniques to training and test distributions.

  • The final step, if applicable, is to rebalance your datasets:

    • If the model performance on the test & validation set is significantly better than the performance on the test set, you over-fit to the validation set.

    • When it does happen, you can recollect the validation data by re-shuffling the test/validation split ratio.