The techniques described (flipping, rotation, zooming) are forms of data augmentation. The primary purpose of data augmentation is to artificially increase the size and diversity of the training dataset by creating modified copies of existing images. This process exposes the model to a wider variety of data, helping it learn more robust and invariant features. As a result, the model's ability to generalize to new, unseen data is improved, and the risk of overfitting is reduced.

A. Image transformations are applied to the data, not the model. They do not simplify the model's architecture.

C. These transformations intentionally introduce variability (e.g., different angles, positions), which is the opposite of ensuring perfect alignment and uniformity.

D. Data augmentation adds a computational step to the data loading pipeline and increases the effective dataset size, which typically increases, rather than reduces, the overall training time and resources.

1. NVIDIA Deep Learning Institute (DLI): In the "Fundamentals of Deep Learning" course, data augmentation is introduced as a key technique to prevent overfitting by artificially expanding the dataset. The course explains that transformations like random rotations, shifts, and flips create new training samples, forcing the model to learn more general features.

Source: NVIDIA DLI, Fundamentals of Deep Learning, Module on "Training Neural Networks", Section on Regularization and Data Augmentation.

2. Stanford University Courseware (CS231n): The course notes for "CS231n: Convolutional Neural Networks for Visual Recognition" detail data augmentation as a common and effective regularization strategy. It states, "The most common way to regularize a network is to augment the data," and lists horizontal flips, random crops, and color jittering as primary examples used to "artificially enlarge the dataset."

Source: Stanford CS231n Course Notes, "Setting up the data and the model", Section on Data Augmentation. (Available on the official course website).

3. Peer-Reviewed Academic Publication: A foundational survey on this topic confirms the purpose. The authors state, "Data augmentation is a data-space solution to the problem of limited data... The goal is that the augmented data will be representative of the variations in the larger, unobserved data distribution, thus enabling the model to generalize better."

Source: Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. Section 2: "Image Data Augmentation".

DOI: https://doi.org/10.1186/s40537-019-0197-0