ADAMNet: Improving imbalanced defect classification with Anomaly-Driven Attention Maps

Key Points

• Improved defect classification observed with anomaly-driven attention maps, enhancing model effectiveness.
• Accuracy increased by up to 20% compared to traditional methods, indicating significant performance gains.
• Assessment using advanced machine learning algorithms focused on imbalanced defect data for optimization.
• Highlights the need for innovative approaches in defect detection to ensure reliable outcomes in real-world applications.