Demystifying Neural Style Transfer: A Survey of Prompt Learning Methods

Balancing Simplicity and Thoroughness: While continual learning is a complex topic, it is essential to provide a clear understanding of the concepts involved. By using everyday language and engaging metaphors or analogies, we can make the topic more accessible and easier to comprehend. However, it is also crucial to provide enough details to capture the essence of the article without oversimplifying the concepts.
Conclusion: In conclusion, continual learning with pre-trained models is a promising approach to improve model performance on new tasks while preventing catastrophic forgetting of previous ones. By understanding the two strategies for building robust continual learning models and demystifying complex concepts through analogies and metaphors, we can develop more effective and efficient machine learning models in the future.