Data generation in machine learning involves creating or manipulating data
to train and evaluate machine learning models. The purpose of data generation
is to provide diverse and representative examples that cover a wide range of
scenarios, ensuring the model's robustness and generalization.
Data augmentation techniques involve applying various transformations to
existing data samples to create new ones. These transformations include:
random rotations, translations, scaling, flips, and more. Augmentation helps
in increasing the dataset size, introducing natural variations, and improving
model performance by making it more invariant to specific transformations.
The dataset contains GENERATED USA passports, which are replicas of
official passports but with randomly generated details, such as name, date of
birth etc. The primary intention of generating these fake passports is to
demonstrate the structure and content of a typical passport document and to
train the neural network to identify this type of document.
Generated passports can assist in conducting research without accessing or
compromising real user data that is often sensitive and subject to privacy
regulations. Synthetic data generation allows researchers to develop and
refine models using simulated passport data without risking privacy leaks.
