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

Question 1

What is the primary purpose of data augmentation in CNN training?

Accepted Answer

To artificially expand the training dataset and improve generalization

Answer

Data augmentation artificially expands the training dataset by applying transformations, helping CNNs generalize better to unseen data.

Question 2

Which augmentation technique randomly flips an image along its vertical axis?

Accepted Answer

Horizontal flip

Answer

A horizontal flip mirrors an image left-to-right along the vertical axis, creating a natural augmentation for many image classification tasks.

Question 3

What does the 'random crop' augmentation technique do during CNN training?

Accepted Answer

Crops a random sub-region of the image for training

Answer

Random cropping extracts a randomly positioned sub-region of the input image, forcing the CNN to learn from different spatial locations.

Question 4

What is Mixup augmentation in CNN training?

Accepted Answer

Creating synthetic samples by linearly interpolating between two training images and their labels

Answer

Mixup blends two training images and their corresponding labels by a random ratio λ, encouraging the model to learn smoother decision boundaries.

Question 5

What does the CutOut augmentation technique do?

Accepted Answer

Randomly masks out a square region of the input during training

Answer

CutOut randomly zeroes out a square patch in the training image, forcing the network to use context from the entire image rather than relying on a single discriminative region.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

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