The video tutorial discusses image augmentation techniques in Keras, emphasizing the use of custom data generators for training. It highlights the importance of applying augmentation during the training phase and provides a code implementation guide. The tutorial also advises using the AUC curve over accuracy for evaluating model performance, especially with imbalanced data, and introduces a callback function to store AUC values during training.

Which of the following is NOT an image augmentation technique mentioned in the video?

• Rotation

• Color inversion

• Shifting

• Horizontal flipping

What is the primary purpose of using a data generator in the context of image augmentation?

• To improve the resolution of images

• To apply transformations to images during training

• To reduce the size of the dataset

• To automate the process of data labeling

Why is it important to apply augmentation only during the training phase?

• To maintain the original dataset size

• To prevent overfitting on the validation set

• To save computational resources during testing

• To ensure the model is exposed to varied data

What is a potential drawback of using accuracy as a metric for model evaluation?

• It is difficult to calculate

• It can be misleading for imbalanced datasets

• It requires a large amount of data

• It is not supported by most libraries

What is the benefit of using the AUC curve over accuracy?

• AUC is easier to interpret

• AUC provides a single threshold for decision making

• AUC is more reliable for imbalanced datasets

• AUC requires less computational power