What is Data Augmentation in Deep Learning?

The accuracy of deep learning models depends upon the quality, quantity, and contextual meaning of the training data set. However, the scarcity of this up-to-the-market data set is one of the major challenges in building DL models. 

Producing a data set to feed the training model could be a time-consuming and costly affair for organizations. That is why the AI teams leverage the benefits of Data Augmentation techniques. 

Data Augmentation is a set of techniques that enable AI teams to artificially generate new data points from the data that already exists. This practice includes making small changes to the data (which could either be a text, audio, or visual), generating diverse instances, and expanding the data set to have improved the performance and outcome of the deep learning model.

For example, Data augmentation methods reduce data overfitting which significantly improves the accuracy rate and generalizes the output of a model. 

Data overfitting happens when a model is trained too well for a set of data. In this case, the model learns the noise and detail of the data to an extent that it starts to impact the performance of the model on new data. This means that the noise and fluctuations in the training data are learned as concepts by the model. 

When a new set of data is added to the model, the learned concepts do not apply to it, which negatively impacts the ability of a model to generalize. This is generally the problem with small data sets. The smaller the data sets, the more is the control of networks over it. But when the size of a data increases through augmentation, the network doesn’t overfit the training data set and is thus forced to generalize.

Data augmentation technique is adopted in almost every deep learning application such as image classification, object detection, natural language processing, image recognition, semantic segmentation, etc. 

Data Augmentation Techniques to Expand Deep Learning Data Set

For augmenting the deep learning data sets, there are several techniques that can be adapted based on the type of data. We will discuss some of the techniques that can be used for augmenting images, text, and audio data.  

Image Augmentation: 

If there are images in the data set, the following techniques can be used for expanding the size of the data: 

• Geometric transformations: The images can be randomly flipped, cropped, scaled, or rotated

                                                                                                                                   Random rotation of an image | Github

• Color space transformations: Changes are made in RGB colors or colors are intensified

• Kernel filters: Images are sharpened or blurred 

• Image mixing: Images are mixed with one another

Text Data Augmentation: 

Text data augmentation can be done at the character level, word level, phrase level, and sentence level. Changes in text data can be made by word or sentence shuffling, word replacement, syntax tree manipulation, etc. Following are some of the techniques that are used for augmenting text data:

• Easy Data Augmentation (EDA)

In this method to augment data, some easy text transformations are applied. For example, a word is randomly replaced with a synonym. Two or more words are swapped in the sentence. For an NLP solution, the EDA technique helps in: 

  1. Replacing words with synonyms
  2. Words or sentence shuffling
  3. Text substitution
  4. Random insertion, deletion, or swapping of words

• Back Translation 

Back translation, also known as reverse translation is the process of re-translating content from a target language back to its source language. This leads to variants of a sentence that help the model to learn better. 

• Generative Adversarial Networks (GAN)

GAN is an unsupervised learning network that involves automatically discovering and learning the regularities or patterns in input data. The model thus generates new examples that could have been apparently drawn from the original data set.

Audio Data Augmentation 

For audio data augmentation, noise injection, shifting, changing the speed of the tape, etc. are some of the techniques used for augmenting data.

                                                                                  Audio augmentation | Source: Github

Data Augmentation: Use Cases 

The data augmentation technique is highly beneficial in cases when the data set is small in size and there are constraints to curate/stretch it. For example, the healthcare industry needs to artificially augment the data in most scenarios. 

• The healthcare industry abides by regulations and compliances where a patient’s consent is prioritized to use his data for research or any other purpose.
  
  
• Not every patient’s data can be utilized for training a model. Depending upon the diagnosis, health history, and treatment; most of the data gets filtered from being trained.
  
  
• There are rare diseases for which enough data is not available to train a model. In such instances, augmenting data with variants of the existing one helps to improve the performance of the model. 

That said, data augmentation is not limited to any industry. This technique can be adopted for any NLP or image recognition application where there ain’t ample data available to train a model. 

Data Augmentation for Neural Networks: Challenges Involved

Expanding a data set for improved performance and cost-saving is a good idea until the same is done by optimizing the augmentation cycle. It is a recommended practice to augment a data set in limits to have expected results. 

While attempting to increase the size of the training data set, AI experts might experience the following challenges: 

Impact on Training Duration: Augmenting the data using frameworks such as Keras, Tensorflow, PyTorch, etc. can impact the training duration of the data set. This is because new data is generated and added to the dataset will increase the efforts and time to train the data. 

When data is added for training, it creates artificial clusters. In Deep Learning, clustering is a fundamental data analysis technique. While these clusters make learning efficient, they might not generalize the data efficiently. Therefore, the model might require training from scratch to achieve satisfactory results. It is recommended to augment the training data set with the optimum amount of data to have desired results and save time. 

Impact of Performance Stability: Augmenting different data sets can lead to varying performance stability of the neural network. It’s the ability of a network to handle noise in the data set that has a positive impact on the performance of the network during the training phase.  

It is important to select the correct parameters with which the dataset needs to be augmented. This will help to stabilize the augmented dataset as fast as possible and lessen the noise that usually impacts the performance. 

Identifying Biased Dataset: A biased AI system is an anomaly in the deep learning algorithms that results in low accuracy levels, skewed outcomes, and analytical errors. Before executing data augmentation, it is necessary to test the data set against ethical AI principles as an anomaly, when multiplied can lead to a high-inaccuracy rate in the output. 

Conclusion: 

For augmenting data sets, there are several libraries that are available to the AI software development teams. Augmentor, Torchvision, imgaug are some of the libraries for image augmentation. Similarly, there is NLPau for creating a variety in the text data sets. 

Data augmentation is a significant practice in an AI software development cycle. At Daffodil, our AI specialists practice this technique to create accurate and performance-rich ML and NLP algorithms. 

However, to make the most of this data expansion technique, it is important to augment the data qualitatively. If your augmentation efforts are not reaping the expected accuracy and performance level, then there is an opportunity to connect with our experts who can help your team achieve the expected development outcome.