Method and apparatus with defect detection

An electronic apparatus, the apparatus comprising:

• a processor configured to:
• perform an iterative training operation of a defect detection model, including:
• randomly assign a label for a detected defect pattern to training data responsive to the detected defect pattern being determined to be a defect pattern that is not among the training data;
• dependent on the label being determined to be a new label type, generate an importance score, which represents a frequency of an occurrence of the defect pattern;
• and
• execute the training of the defect detection model using the defect data of the defect pattern when the importance score exceeds the first threshold value;
• and
• delete the defect data when the importance score does not exceed the first threshold.

A processor-implemented method, the method comprising:

• randomly assigning a defect label to a determined unknown detected defect type not among defect types in a training data set;
• generating an importance score for the unknown detected defect type;
• selectively training a machine learning model using the unknown detected defect type when the importance score meets a first threshold value;
• and
• deleting the unknown detected defect type when the importance score fails to meet the first threshold value.