Data augmentation creates new data points by altering existing data or generating new ones, while synthetic data is artificially generated without real-world images. Augmented data involves minor geometric transformations, while synthetic data is often produced by Generative Adversarial Networks.
Data augmentation alters existing data or generates new data points with minor geometric transformations.
Synthetic data is artificially created without real-world images, often using techniques like Generative Adversarial Networks.
Augmented data provides variations, while synthetic data offers novel samples.
Both techniques enhance image annotation by increasing diversity and improving model performance.
Data augmentation focuses on expanding the existing dataset by applying various geometric transformations to the original images. These transformations, such as rotations, translations, flips, and scaling, introduce variations while preserving the underlying information. Augmented data retains the characteristics of real-world images but offers a wider range of perspectives and orientations. By augmenting the dataset, the model becomes more robust, improving its ability to handle diverse scenarios and increasing its generalization capabilities.