The choice between early stopping and training as much as possible while saving checkpoints depends on your goals, resources, and the specific characteristics of your dataset. Below is a breakdown of the trade-offs to help you decide:
Option 1: Early Stopping
Pros
- Prevents Overfitting:
- Stops training when the validation loss plateaus or starts increasing, ensuring the model doesn’t memorize noise in the training data.
- Saves Time and Resources:
- Avoids unnecessary epochs once the model has converged, which is critical for large datasets or expensive cloud training.
- Automated Workflow:
- Requires minimal manual intervention once configured.
Cons
- Risk of Early Termination:
- If the validation loss fluctuates (e.g., due to noisy data), early stopping might halt training prematurely before the model fully converges.
- Dependence on Validation Data:
- Requires a well-curated validation set to avoid bias.
When to Use
- When training time/compute costs are a concern.
- When you have a reliable validation set and want to avoid overfitting.
Option 2: Train Longer and Save Checkpoints
Pros
- Flexibility:
- Allows you to analyze intermediate models and select the best one post-training (e.g., the checkpoint with the lowest validation loss).
- Robust Convergence:
- Useful if the loss landscape is complex, and the model might improve after temporary plateaus.
- No Validation Set Dependency:
- Helpful if validation data is scarce or unreliable.
Cons
- Resource-Intensive:
- Requires more epochs (and potentially more cloud compute costs).
- Manual Effort:
- You must manually evaluate checkpoints to find the best model.
- Risk of Overfitting:
- If unchecked, the model might overfit the training data in later epochs.
When to Use
- When you want to analyze model performance at different stages.
- When you suspect the model might improve after temporary plateaus.
- When validation data is noisy or limited.
Hybrid Approach
A balanced strategy combines both:
- Train for Many Epochs and save checkpoints periodically.
- Track Validation Loss during training (even if early stopping is disabled).
- Select the Best Checkpoint based on validation loss, not just the final epoch.
Final Recommendation
- For Prototyping on Small Data:
- Use checkpoints without early stopping to explore model behavior and manually inspect results. Overfitting is less critical at this stage.
- For Cloud Training:
- Use early stopping to save costs and prevent overfitting, especially with large datasets.
- If you need flexibility, combine checkpoints with validation loss tracking and select the best model post-training.
By saving checkpoints with validation loss metadata, you retain the flexibility to choose the best model later while mitigating the risk of overfitting. Let me know if you need further refinements!
