To mitigate model collapse, AI practitioners can use various techniques, such as:
- Regularization techniques, such as dropout, L1/L2 regularization, and early stopping
- Data augmentation and data generation
- Ensemble methods, such as bagging and boosting
- Transfer learning and fine-tuning
- Model selection and hyperparameter tuning
- Monitoring and evaluating model performance using metrics such as accuracy, precision, recall, and F1-score
By understanding and addressing model collapse, AI practitioners can develop more robust, accurate, and reliable models that can effectively learn from data and generalize to new situations.