Model collapse can be caused by various factors, including:
- Poor model architecture or design
- Limited training data or biased data
- Insufficient regularization or regularization techniques
- Over-reliance on a single feature or input
- Lack of diversity in the training data
Model collapse can have significant consequences, such as:
- Poor performance on test data
- Lack of generalizability to new situations
- Inability to adapt to changing data distributions
- Reduced accuracy and reliability