Deep Learning Model Fixing: A Comprehensive Study
Analysis
This paper is significant because it provides a comprehensive empirical evaluation of various deep learning model fixing approaches. It's crucial for understanding the effectiveness and limitations of these techniques, especially considering the increasing reliance on DL in critical applications. The study's focus on multiple properties beyond just fixing effectiveness (robustness, fairness, etc.) is particularly valuable, as it highlights the potential trade-offs and side effects of different approaches.
Key Takeaways
- •Provides a large-scale empirical study of 16 DL model fixing approaches.
- •Evaluates fixing effectiveness, robustness, fairness, and backward compatibility.
- •Highlights trade-offs between different fixing approaches.
- •Model-level approaches show better fixing effectiveness.
- •No single approach is optimal across all properties.
Reference
“Model-level approaches demonstrate superior fixing effectiveness compared to others. No single approach can achieve the best fixing performance while improving accuracy and maintaining all other properties.”