How to improve deep learning model?
Improve - Troubleshooting
- 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.