CNN - Convolutional Neural Networks Data Augmentation Techniques Questions and Answers Flashcards

What does the ColorJitter augmentation randomly alter in a training image?

Answer: Brightness, contrast, saturation, and hue

ColorJitter randomly perturbs brightness, contrast, saturation, and hue to make models robust to varying lighting and color conditions.

What is RandAugment?

Answer: An automated augmentation policy that uniformly samples from a set of augmentation operations

RandAugment simplifies augmentation search by randomly applying N operations from a fixed set, each at a global magnitude M, reducing the search space significantly.

What is Test-Time Augmentation (TTA) used for in CNN inference?

Answer: Applying multiple augmentations to test images and averaging predictions to improve accuracy

TTA applies several augmented versions of each test image, collects the model's predictions for each, and averages them to produce a more robust final prediction.

What does AutoAugment do in the context of CNN training?

Answer: Searches for an optimal augmentation policy using reinforcement learning

AutoAugment formulates the augmentation policy search as a reinforcement learning problem, where the controller learns which transformations and magnitudes maximize validation accuracy.

What is the 'Random Erasing' augmentation technique?

Answer: Randomly selecting a rectangle in the image and replacing it with random pixel values

Random Erasing selects a random rectangular region in the training image and replaces it with random noise or a constant value, helping the model become robust to partial occlusion.

What risk does overly aggressive data augmentation introduce during CNN training?

Answer: It can create unrealistic training samples that hurt model performance

Extreme augmentations can generate semantically invalid images that mislead the network, causing it to learn incorrect feature representations and degrading accuracy.