Using synthetic data like JitterWeb has certain limitations that could impact the model's performance. One limitation is the potential lack of diversity and complexity in the synthetic data compared to real-world UI examples. Synthetic data may not fully capture the nuances and variations present in actual UI designs, leading to biases and inaccuracies in the model's assessments. Additionally, synthetic data may not accurately reflect the dynamic nature of user interactions and preferences in real-world scenarios.
To improve the model's performance, incorporating more real-world UI examples is crucial. By augmenting the dataset with a diverse range of actual UI designs from different applications and platforms, the model can learn from a more representative and comprehensive set of examples. Real-world data can provide valuable insights into current design trends, user preferences, and industry standards, enhancing the model's ability to assess and provide feedback on authentic UI designs.
Furthermore, incorporating real-world UI examples can help address issues of bias and generalization that may arise from relying solely on synthetic data. By training on a mix of synthetic and real-world data, UIClip can achieve a more robust and accurate understanding of UI design quality and relevance.