Model collapse can manifest in various ways, such as:
- Overfitting: The model becomes too specialized in the training data and fails to generalize to new, unseen data.
- Underfitting: The model is too simple and fails to capture the underlying patterns in the data, resulting in poor performance.
- Mode collapse: The model produces a limited set of outputs or modes, rather than exploring the entire output space.
- Catastrophic forgetting: The model forgets previously learned information when new data is added to the training set.