RandAugment: A Method for Automated Data Augmentation
Data augmentation is a technique used in machine learning where additional training data is created from existing data by applying various transformations, such as flipping, rotating, or changing contrast. This helps to improve the performance of machine learning models by providing them with more diverse and representative examples to learn from. However, manually applying these transformations to a large dataset can be time-consuming and expensive. This is where RandAugment comes in - it's an automated data augmentation method that can apply a series of transformations to images without requiring any human input.
Understanding the Search Space for Data Augmentation
The search space for data augmentation in RandAugment is controlled by two hyperparameters: N and M. N specifies the number of transformations to apply sequentially, and M sets the magnitude of each transformation. The larger the value of M, the more intense the effect of the transformation will be. For example, if M is set to 10 for the 'brightness' transformation, the image will be brightened significantly. On the other hand, if M is set to 1 for the same transformation, the effect will be much more subtle.
To reduce the parameter space and maintain image diversity, RandAugment uses a parameter-free procedure where transformations are selected with a uniform probability of 1/K. Here K refers to the number of transformation options available. For example, if there are 10 possible transformations, each transformation will have a 10% chance of being selected. Given N transformations for a training image, RandAugment can express KN potential policies. This means that there are a large number of possible combinations of transformations that can be applied to each image, making the dataset much more diverse.
Transformations Used in RandAugment
The transformations used in RandAugment include:
- Identity transformation
- AutoContrast
- Equalize
- Rotation
- Solarization
- Color jittering
- Posterizing
- Changing contrast
- Changing brightness
- Changing sharpness
- Shear-x
- Shear-y
- Translate-x
- Translate-y
Each of these transformations has a different effect on the image. For example, rotation can be used to make the object in an image appear from a different angle, while changing the contrast can increase or decrease the difference between the lightest and darkest parts of the image. By randomly applying different transformations to an image, RandAugment can create a diverse range of training data to improve the performance of machine learning models.
RandAugment is a powerful method for creating diverse training data for machine learning models. By randomly applying different transformations to images, it can create a wide range of training examples that capture different viewpoints and lighting conditions. This can improve the accuracy and robustness of machine learning models, making them more effective at tasks such as image recognition or object detection.
