Data Augmentation in Deep Learning

Data Augmentation

• Data augmentation is a technique used to increase the size of a dataset by applying transformations to existing data, thereby reducing overfitting and improving model performance.
• Augmented data is distinct from synthetic data, which is entirely generated data that does not exist in the original dataset.

Image Augmentation

• Geometric transformations in image augmentation include:
  • Rotation
  • Scaling
  • Translation
  • Flipping
• Color space transformation involves converting images between different color spaces (e.g., RGB to grayscale) to simulate varying lighting conditions or sensor responses.

Biometrics

• Biometric systems use automated measurement of physiological and/or behavioral characteristics to determine or authenticate identity.
• Identification involves determining an individual's identity from a dataset, whereas authentication verifies an individual's claimed identity.
• In biometric systems, 'unimodal' refers to the use of a single biometric trait (e.g., face recognition) for identification or authentication.
• 'Automated measurement' primarily indicates the use of sensors or cameras to capture biometric data.
• Verification systems, which authenticate a claimed identity, have the advantage of being more efficient and accurate than identification systems, which require searching the entire database to determine an individual's identity.

Test your knowledge of data augmentation in deep learning with this quiz! Explore the concept of creating modified copies of a dataset using existing data and understand the difference between augmented and synthetic data. Perfect for deep learning enthusiasts and data scientists.