Data Augmentation in Deep Learning

Questions and Answers

To prevent models from overfitting

Augmented data is derived from original data with minor changes

Randomly flip, crop, rotate, stretch, and zoom images

Randomly change RGB color channels, contrast, and brightness Signup and view all the answers

To prevent models from overfitting Signup and view all the answers

Biometric Signup and view all the answers

Many-to-one mapping vs. one-to-one mapping Signup and view all the answers

Single mode of biometric authentication Signup and view all the answers

No human involvement in measurement Signup and view all the answers

More accurate Signup and view all the answers

Study Notes

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.

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.

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.