Data augmentation: Techniques, Benefits and Applications

Data is the key soul of the era. Data drives every decision of the world. But even the data collected is unworthy, if not well organized, separated and selected. In this role, data augmentation is the lead. Data augmentation helps in selecting, blending, highlighting and thus organizing the data. 

 

In this blog we will read about data augmentation. We will also see its working process, applications and benefits of it.

 

(Learn more about Strong AI)

 

 

What is Data Augmentation?

 

Data augmentation comprises two terms data and augmentation. It is a popular technique that is used to increase the generalizability of an overfitted data model. Data augmentation is a part of data analysis. 

 

It is the set of techniques that is used to increase the amount of data by adding modified copies of already existing data. Sometimes, it creates newly synthetic data from the existing data. 

 

Data augmentation acts as a regularizer and assists in managing the overfitting of data. Data augmentation increases the possibility of overfitting the model by generating additional training data and exposing the model to different versions of data. 

 

You might be wondering, what is the exact meaning of overfitting a data model?

 

An overfitted data model is a modeling error in the data sequence that occurs when a function is closely aligned to a limited set of data points.

 

(Suggested blog - Data Labeling)

 

 

Importance of Data Augmentation

 

Now, the next question is, why data augmentation is important. Data augmentation is an element of machine learning. Machine learning is filled with diversifications and its domain increases rapidly. And data augmentation acts as a tool against these challenges. 

 

It is useful in improving performances and outcomes of machine learning models. The data augmentation tools make the data rich and sufficient and thus makes the model perform better and accurately. Data augmentation techniques reduce the operational costs by introducing transformation in the datasets. 

 

Data augmentation assists in data cleaning, which is essential for high accuracy models. Data augmentation makes machine learning more robust by creating variations in the model.

 

 

What are the techniques used in data augmentation?
 

Before jumping to the process or working of data augmentation. Let us understand the techniques involved in data augmentation and the concept behind them. Following are the advanced data augmentation models:

 

Adversarial training

 

Also known as adversarial machine learning. It generates adversarial examples. These examples disrupt machine learning models. Later it injects them into datasets.

 

GANs

 

GANs expand as generative adversarial networks. These algorithms learn patterns from input datasets. It then creates new examples which resemble training data.

 

Neural style transfer

 

The neural style transfer is a technique that blends content image and style image. It also performs a reverse function and separates style from content.

 

Reinforcement learning

 

Reinforcement learning helps the software agents to make decisions in a virtual environment.
 

Working of data augmentation

 

The process of data augmentation varies from task to task. There are traditional and modern techniques that are used for image recognition and natural language processing.

 

For image classification

 

The data augmentation helps in visual transformations. The transformation that is mainly focused on image classification are flipping, color modifications, cropping, rotation, geometric transformation, padding, re-scaling, zooming, gray scaling, darkening and brightening, random erasing etc.

 

For introducing new synthetic images in image classification, the data augmentation process uses various techniques such as generative adversarial networks and unity game engine.

 

For signal processing

 

Data augmentation is also used in several signal processing. Examples of such signals are electromyography and electroencephalography.  

 

For signal processing data augmentation techniques such as generative adversarial networks and deep convolutional neural networks. Data augmentations achieve signal identification by statistical machine learning models. These models trained on the synthetic domain could classify the human data.

 

For speech recognition

 

Data augmentation assists the improvement of speech recognition by transferring learning from synthetic data which has been generated via character-level recurrent neural network.

 

For natural language processing (NLP)

 

Data augmentation for natural language processing works by augmenting the text data. Though, it isn’t that popular due to the complex nature of the process. Common operations include synonym replacement, word insertion, word swap, word deletion, back translation and contextualized word embeddings. 

 

(Recommended blog - Data Science in Digital marketing)

 

Advantages and Disadvantages of Data Augmentation

 

Advantages of Data Augmentation

 

The advantages of data augmentation are as follows:

 

1. It reduces the cost of collection of data.

2. It reduces the cost of labelling data.

3. It improves the model prediction accuracy.

4. It prevents data scarcity.

5. It frames better data models.

6. It reduces data overfitting.

7. It creates variability and flexibility in data models.

8. It increases the generalization ability of the data models.

9. It helps in resolving the class imbalance issue in the classification.

 

Limitations of data augmentation

 

Along with the advantages, let us discuss the limitations and challenges of data augmentation too. The limitations of data augmentation are as follows:

 

1. Requires evaluation systems for quality of augmented datasets.

2. Requires new research to create new or synthetic data with advanced application.

3. Application of few data augmentation techniques like GANs is quite challenging.

4. Identification of optimal data augmentation strategy is another challenge.

5. Augmented data contain biases if the real dataset contains the same.

 

(Suggested Read - Data Integration)

 

 

Applications of Data Augmentation

 

In the medical field, data augmentation is used in the medical imaging domain. In the medical field, it is used for the identification of rare diseases. Examples include:

 

1. For identification of brain tumors.

2. For differential data augmentation.

3. For automated data augmentation.

4. For performing semi-supervised tasks.

 

In technical field, data augmentation has following applications:

 

1. Deep learning software.

2. Natural language processing.

3. Image recognition

4. Machine learning software.

 

(Suggested blog - Ethnographic Data)

 

Thus, data augmentation plays a key role in data management. But selecting a good data augmentation technique as per your business and data requirements is important. There are several agencies which support the same. Choose wisely, earn nicely!