A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%. What should the specialist consider to fix this issue?
A) A longer training time
B) Making the network larger
C) Using a different optimizer
D) Using some form of regularization

Question

Quizplus · Accepted Answer

The gap between training and validation accuracy suggests overfitting. Regularization can help reduce overfitting by penalizing complex models, thus improving validation performance.

A Manufacturing Company Asks Its Machine Learning Specialist to Develop