August 12, 2022

Data Augmentation Techniques for Numerical Data Analysis

In general, deep learning structures come equipped with underlying data growth utilities. On the other hand, such may be deficient in some required utility or inefficient in delivering that usefulness. Here comes data augmentation to the rescue.

Machine learning applications are continually expanding and diversifying across all areas of technology and are being applied to many issues in real life. The number, quality, and variety of the data used in machine learning models all have a role in the models' overall success. The selection of the target data, which is to be picked from the initial data collection, is a vital step that must be taken to improve the algorithm's dependability. Data may be collected in symbolic and numeric characteristics, which can be obtained from humans to sensors with varying degrees of complexity and trustworthiness. Data can be acquired in the form of symbolic and numeric attributes.

What is Data Augmentation?

In data analysis, “data augmentation” refers to the procedures utilized to expand the measure of data by adding somewhat revised copies of previously existing data or recently made synthetic data based on existing data. This is accomplished by adding "augmented" copies of previously existing data. Training a machine learning or artificial intelligence model is a regularizer and helps reduce overfitting. The quantity and variety of data accessible to the model when being introduced significantly impact the accuracy of the predictions that supervised deep learning models may make. There is a variety of data augmentation techniques to show the working relationships between deep learning and various other models. The relationship between deep learning models and the amount of training data required is analogous to the relationship between rocket engines and the enormous amount of fuel needed for the rocket to complete its mission. In other words, the relationship between the two concepts is very similar (success of the deep learning model).

DL models trained to attain high performance on complex tasks generally include a substantial percentage of hidden neurons. The number of trainable parameters likewise rises in proportion to the network’s total number of hidden neurons. Computer Vision models considered to be state-of-the-art, such as RESNET (60M) and Inception-V3 (24M), have many parameters on the order of 10 million in total. It is one hundred million in Natural Language Processing models such as BERT (340M). The deep learning models that are taught to do complex tasks with a high level of accuracy, such as object identification or language translation, include many parameters that may be tuned. During the training phase, they must learn the values for many parameters, which requires a substantial quantity of data.

Put another way, the quantity of necessary data is directly proportional to the number of learnable parameters included in the model. The work’s difficulty level is directly proportional to the number of parameters. When working on complicated tasks, such as differentiating a weed from a crop or determining whether or not a patient is unique, it may be complicated to collect significant volumes of data necessary to train the models. This is because vast amounts of data are required to prepare the models. Even while transfer learning methods have the potential to be employed to great advantage, the obstacles involved in making a pre-trained model function for particular applications are challenging. Hence, data augmentation techniques can be of so much use in the digital world.

Applying a variety of transformations to the data already available to generate new data is yet another strategy for addressing the issue of insufficient data. Generating new data by combining existing data with newly gathered information is called "Data Augmentation." Data augmentation is a strategy that may be used to handle not just the needs but also the variety of the training data and the volume of the data. In addition to these two solutions, the usage of data augmentation techniques may also be used in classification jobs to solve the class imbalance issue.

Why Data Augmentation Techniques are Growing at a Rapid Pace?

Even using transfer learning strategies, it may be challenging to overcome the obstacles involved in making a machine learning (ML) model operate on specific tasks and supplying it with the data it needs.

The significance of data augmentation becomes immediately apparent in this context. With the assistance of various data augmentation strategies, it is possible to meet both of the requirements outlined in the introduction: the diversity of the data and the sheer amount of it. Nevertheless, the significance of an enriched data structure is not limited to only that aspect. Even problems with imbalance categorization may be solved with its assistance. The most well-known numerical data augmentation strategies, such as SMOTE and SMOTE NC, are used to remedy class imbalance issues.

When we compare quality models for different applications with and without augmentation, augmentation’s major significance becomes immediately apparent. The performance of image classification without it is around 57 percent. Still, the version of image classification using basic and GAN-based techniques is 78 percent for the first approach and 85 percent for the second method. Text categorization also significantly improves after undergoing data augmentation, going from 79 percent with it to 87 percent without it.

Many case studies suggest that data augmentation approaches boost overall performance, and a variety of augmentation strategies have a favorable impact on the model. A significant step has been made in developing augmentation techniques for basic, unstructured data such as photographs. These techniques include making straightforward adjustments, such as rotating, mirroring, flipping, cropping, scaling, translating, altering the brightness, or color casting an image. Data augmentation techniques are gaining prominence day by day.

These procedures, despite their ease of use and shown efficacy, are not without their share of drawbacks. When these alterations are performed to the original picture, there is a potential that the image may lose some of its most distinctive characteristics. This is an essential concern. As a result, more complex methods, such as Neural Style Transfer, Generalized Adversarial Networks (GAN), and Adversarial Training, are being applied to produce more applicable modifications.

Numerical Data Augmentation Technique

Depending on the kind of information employed for Deep Learning or DL applications, several data augmentation strategies may be implemented to improve numerical data. In order to increase the scope of simple mathematical data, prominent processes such as SMOTE NC or SMOTE are useful. The primary purpose of using these tactics is to remedy the problem of unequal class representation in assignment classifications.

In the case of unstructured data, such as text and pictures, data augmentation methods may provide information independently from basic modifications to a neural network. This is since the application is designed in a myriad of different ways. Deep Neural Networks are trained to extract the content from one picture and the style from another image in the process of neural style transfer. After this, the augmented image is composed utilizing the content and style that was extracted. The input picture is "painted" in the manner of the style image, and the augmented image is then altered to seem exactly like the input image.

Types of Numerical Data Augmentation

  1. Enhancement based on a GAN, which consists of a discriminator and a generator. Fake pictures are supposed to be produced by the second trained neural network, as the name of the network indicates. The discriminators' job is to determine which photographs are authentic and which are fraudulent.
  2. Adversarial Training is a technique that involves modifying pictures in order to make them suitable for use as training data. In order for the model to produce a variety of distinct augmented pictures, it is necessary to apply these transform images, also known as masks, on the input image.
  3. During this process, neural style transfer will blend the structure of one picture with the design of another image. Utilizing a variety of photos in this manner creates an enhanced image. The "new" style from the second picture is the only thing that differentiates the augmented image from the original (input) image. The augmented image is identical to the original (input) image.

Developing high-performance machine learning models may be sped up significantly with the use of automation of the data augmentation techniques...

Taking an existing data structure and reworking it somewhat to better accommodate your needs is called "data structure augmentation." This makes it possible for you to take advantage of a crisp stock data structure that almost. However, it doesn't reasonably address your worry and lacks that one last piece of information that makes it effective in resolving the issue. To create effective data augmentation, you need in-depth research on your creativity, problem-solving skills, and industry expertise. Data augmentation techniques are on the rise and will gain more prominence daily.

Final Verdict

VisionERA is a cutting-edge Intelligent Document Processing (IDP) platform that allows you to do data augmentation swiftly and accurately. VisonERA offers the best performance in this niche and incorporates a system for ongoing learning. It can quickly adapt to your company’s requirements and carry out operations with steadily improving speed and precision.

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