Data Argumentation
Introduction
Deep learning uses data augmentation to increase the quantity and diversity of training data without gathering extra data. This is accomplished by modifying existing data by rotating, scaling, flipping, cropping, adding noise, or altering colors to provide new variations from which the model may learn.
The fundamental goal of data augmentation is to increase deep learning models' generalization capacity by avoiding overfitting and enhancing the model's robustness to perturbations in the input data. The model can handle variances in real-world data better if it is trained on a bigger and more diversified set of instances.
Data augmentation is commonly employed in image recognition tasks such as object detection, segmentation, and classification, but it can also be applied to text, audio, and time-series data.
Many libraries and tools in popular deep learning frameworks, such as TensorFlow and PyTorch, include built-in support for data augmentation, making it simple to apply these alterations to your data during training. However, in order to achieve the best possible outcomes, it is critical to select the optimal collection of transformations based on the individual characteristics of your data and the task at hand.
In simple words:
Data augmentation is a machine learning and deep learning technique that uses modified versions of existing data to expand the amount and diversity of training data. This is accomplished by transforming the original data using various transformations such as rotating, scaling, flipping, cropping, adding noise, or altering colors.
The purpose of data augmentation is to improve deep learning models' generalization capacity, prevent overfitting, and improve the model's ability to handle variances in input data. Data augmentation is commonly employed in picture identification tasks, but it can also be used on text, speech, and time-series data.
Type of Methods
There are various methods used for data argumentation in both deep learning and machine learning which will depend on the particular problem at hand. The most commonly used methods are as followed below:
- Geometrical Transformation
- Color Transformation
- Adding noise
- Mixup
- cutout
- Random Erasing
- Style Transfer
1. Geometric transformations change the spatial arrangement of pixels in an image without changing their color or intensity. Geometric transformations include rotating the image by a specific angle, scaling it to make it larger or smaller, flipping the image horizontally or vertically, and cropping the image to focus on a specific area of interest.
2. Color transformations change the color and brightness of pixels in an image without changing their spatial arrangement. Color modifications involve changing the image's brightness, contrast, saturation, or hue.
3. Adding noise: This entails introducing random fluctuations to an image's pixel values in order to imitate the effects of noise or interference that may exist in real-world data. Mixup is the process of combining two or more photos to create a new image that is a linear combination of the original images. This can aid in the generation of new, diverse examples that the model has never seen before, as well as improve the model's capacity to generalize to new data.
4. Cutout is the process of randomly blocking out a rectangular area of an image in order to simulate occlusions or missing data. This can help the model learn to be more resilient to missing data while also preventing the model from depending too heavily on specific input features.
5. Style transfer refers to the process of transferring the style of one image to another, resulting in the creation of a new image that combines the content of one image with the style of another. This can be used to generate new, synthetic instances that resemble the original data but have different styles or textures.
Methods used in Image Processing
All of these are standard data enhancement techniques used in image processing and computer vision tasks. Here's a quick rundown of each:
- Random Cropping or Shifting
- Slight Rotation
- Color Transform
- Adding Noise
- Scaling
- Stretching or Shearing
1. Random cropping or shifting entails cropping or shifting the input image to a smaller size or moving the image within the frame at random. This can aid in the creation of variations of the input image from which the model can learn, as well as in reducing overfitting by pushing the model to learn from diverse regions of the image.
2. Slight Rotation: This includes rotating the input image by a slight angle at random. This can help the model learn to detect objects in varied orientations and can also help the model generalize to new data.
3. Color Transform: This entails adjusting the brightness, contrast, saturation, or hue of the input image using various color transformations. This can assist the model in learning to distinguish items in diverse lighting conditions or color schemes.
4. Adding Noise: This entails introducing several types of noise into the input image, such as Gaussian noise, salt and pepper noise, or speckle noise. This can aid the model's learning to distinguish objects in noisy or low-quality photos, as well as it's capacity to generalize to new data.
5. Scaling is the process of increasing or decreasing the size of the supplied image. This can assist the model in learning to recognize things at various scales, as well as decrease overfitting by pushing the model to learn from photos of varying resolutions.
6. Stretching or Shearing is the process of stretching or shearing an input image along one or more dimensions. This can aid the model's learning to recognize objects with diverse aspect ratios, as well as its capacity to generalize to new data.
All of these methods are used to generate variations of the input image from which the model can learn and to increase the model's capacity to generalize to new data. Using a combination of these strategies, it is feasible to build a vast and diverse set of training data that will aid the model in performing better on the task at hand.
Basic Methods
The basic methods used in the data argumentation are as follows:
- Flip
- Rotate
- Scale
- Crop
- Translate
1. Flip: This includes horizontally or vertically flipping the supplied image. This can assist the model in learning to distinguish objects orientated in diverse directions.
2. Rotation is the process of rotating the input image by a specific angle, commonly 90, 180, or 270 degrees. This can assist the model in learning to recognize objects in various orientations, as well as decrease overfitting by pushing the model to learn from different sections of the image.
3. Scale: This is the process of increasing or decreasing the size of the supplied image. This can assist the model in learning to recognize things at various scales, as well as decrease overfitting by pushing the model to learn from photos of varying resolutions.
4. Crop: This is the process of reducing the size of the supplied image. This can aid the model's learning of item recognition in cluttered settings or photographs with distracting backgrounds.
5. Translate: This is the process of moving the input image along the x or y-axis. This can assist the model in learning to distinguish things in various places within the image.
All of these methods are used to generate variations of the input image from which the model can learn and to increase the model's capacity to generalize to new data. Using a combination of these strategies, it is feasible to build a vast and diverse set of training data that will aid the model in performing better on the task at hand.
Types of Data Argumentations
There are two different types of Data Argumentations mentioned below:
- Online Data Argumentation
- Offline Data Argumentation
1. Online Data Argumentation: Online data augmentation, on the other hand, entails using data augmentation techniques during training in real time. This means that the training dataset is not pre-processed and is randomly changed and supplied into the model during training. Because it enables random changes to be performed to the data during training, online data augmentation is more adaptable and can be tailored to diverse training settings. However, because the data must be modified in real-time during training, it may be slower than offline data augmentation.
2. Offline Data Argumentation: Before training the deep learning model, offline data augmentation entails pre-processing the full training dataset. This means that data augmentation techniques are performed on the training data before it is written to disk in a new file format. During training, the model reads the augmented data.
Because the augmented data is generated once and kept on disk, it can be swiftly read into memory during training, offline data augmentation is often faster and more efficient than online data augmentation. However, more storage space is required to hold the enriched data.
Key Points to Remember
- Data augmentation is a key deep learning strategy for boosting the volume and diversity of training data, which can enhance model performance and generalization. The following are important considerations for data augmentation:
- Data augmentation's main objective is to introduce new variations to the current training data by using a range of transformations and alterations. This increases the model's exposure to a larger variety of scenarios and enhances its capacity to manage various input patterns.
- Common Transformations: Applying different transformations to the photos, such as rotation, translation, scaling, flipping, shearing, zooming, and cropping, is known as data augmentation. These changes give the model resilience and simulate real-world variability.
- Unpredictability: To produce different samples, the modifications used during data augmentation should have some degree of unpredictability. It is possible to create various augmented versions of each input image using random parameters like rotation angles, translations, or scaling factors.
- Image-Specific Augmentation: Particular transformations may be preferable depending on the properties of the dataset. Elastic deformations, intensity changes, and noise augmentation, for instance, can imitate realistic imaging settings in medical imaging.
- Maintaining Balance: It's crucial to make sure that data augmentation doesn't add a major bias or change the data's class distribution. To prevent any imbalance problems, the enlarged samples should still correspond to the original class proportions.
- Augmentation on the Fly: During training, data augmentation can be used in real-time to produce augmented examples. As only the original data needs to be stored, this conserves memory and storage.
- Other augmentation techniques, such as color jittering, contrast modifications, and image blurring, can be utilized in addition to geometric transformations. These methods can further increase the training data's diversity.
- Evaluation on Original Data: Although data augmentation increases the size of the training dataset, it is crucial to analyze the model's performance on the original, unaltered data in order to verify generalizability and gauge the effect of augmentation.
Conclusion
To summarize, data augmentation is an effective method for boosting the performance of deep learning models. Data augmentation can help to prevent overfitting and increase the model's capacity to generalize to new data by creating variations of the input data by techniques such as flipping, rotating, scaling, cropping, and introducing noise.
Overall, data augmentation is an important tool for deep learning practitioners since it allows them to increase the performance and robustness of their models on a variety of tasks and datasets.
Reference
[1] Shorten and Khoshogoftaar,
Journal of Big Data 2019
