Data Augmentation in Deep Learning

What is the purpose of data augmentation?

What distinguishes augmented data from synthetic data?

What are examples of geometric transformations in image augmentation?

What does color space transformation involve in image augmentation?

When should data augmentation be used?

Which term best describes the process of 'automated measurement of physiological and/or behavioral characteristics to determine or authenticate identity'?

What is the primary difference between identification and authentication in biometric systems?

What is the meaning of 'unimodal' in the context of biometric systems?

In biometric systems, what does 'automated measurement' primarily indicate?

What is the main advantage of verification systems over identification systems in biometrics?

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.