Data is an essential asset of every organization and scientific enterprise. This is important because it enables these enterprises to identify relationships within the organization, identify problems within the institution, and make informed decisions in a bid to achieve results. Equally, it is of great importance in the development of machine learning and artificial intelligence. It is in this light that the effective acquisition and management of data have become expedient.
Further, data augmentation plays a vital role in the acquisition and management of data as it contributes to the development of relevant information while also enhancing data quality. As such, researchers and data scientists have directed various efforts towards promoting data augmentation.
Noteworthy, one of such recent technological emergences aimed at achieving efficiency in data augmentation is the Generative Adversarial Network (GAN). In light of the above, this essay examines Generative Adversarial Networks (GANs) and Data Augmentation in a bid to identify their relationship and relevance to data management.
Data augmentation is a process, technique, or strategy that allows users to significantly improve the variety of available data without the need to collect new data. For example, using a picture as a case study, data augmentation techniques will involve activities such as padding, cropping, and horizontal flipping of the image, among others, to create new images. (Tanka & Aranha, 2019)
As such, data augmentation involves adding value to base data by increasing the data through information from internal and external sources. Noteworthy, this technique is relevant to various types of data across diverse industries. Also, through this technique, the requirement for manual intervention to develop meaningful data and insight into an organization or entity is reduced. (Tanka & Aranha, 2019)
Generative Adversarial Networks (GANs) was first popularized by Ian Goodfellow and his cohort in 2014. Since then, this machine learning framework has become one of the most exciting in recent years. Generally, it is a form of a generative model, meaning it can manufacture new content or data based on existing training data. (Goodfellow et al., 2014)
Similarly, this framework has a wide range of possible applications, including improving or increasing resolution and developing novel molecules for oncology, among others. However, its principal area of use is the formation of new pictures in the fashion, and gaming industry, among others. (Tanka & Aranha, 2019)
Furthermore, a GAN comprises two Artificial Neural Networks (ANNs), namely a Generator and a Discriminator that contends with each other. Generally, a generator manufactures novel datasets, and the discriminator examines those data to ensure their authenticity. As such, while a generator is responsible for creating new data, the discriminator is saddled with examining and ascertaining the quality of data produced. (Tanka & Aranha, 2019)
Noteworthy, these networks are in constant competition with each other, which results in continuous improvements of the Generative Adversarial Network (GAN). Practically, the generator continually improves at distinguishing real data from fake ones. At the same time, the generator follows suit to produce data that is increasingly similar to the actual data and eventually fools the discriminator. (Tanka & Aranha, 2019)
One of the prime possibilities of the Generative Adversarial Network is its use to create simulated data that are hardly distinguishable from the real data and can, as such, augment a dataset. As already established, one of the significant areas of applications for this technology is the generation of images. (Jerry, 2019)
Noteworthy, this process involves the generator taking in any random line and mapping it into an output image depending on the preferred size. Afterward, the image is now processed by the discriminator network, which evaluates the model to determine the degree of semblance with the original copy. (Jerry, 2019)
Also, the discriminator provides the loss action for the gradient descent improvement and backpropagation function that the generator performs. Instructively, this technology produces an exceptional result that improves on the orthodox solutions – such as flipping images, shifting them, and adding noise, among others – to data augmentation. (Jerry, 2019)
Furthermore, this is also relevant to medical imaging. Today, marked medical imaging is expensive and scarce to generate, thanks in no small way to the vast amount of required data. However, with the application of GANs, improved results and lower cost is guaranteed. For instance, according to research carried out by Sandfort et al., the use of a CycleGAN resulted in a dramatic increase from 0.09 to 0.66 concerning non-contrast out-of-distribution pictures.
Again, GANs are highly relevant in deep learning and machine learning. The emergence of increasing attention to smart technologies, especially in artificial intelligence (AI), has made data even more critical. This importance is because, for machines to function like the human brain effectively, there is the need to teach them how. Necessarily, this is only achievable through data on how the human brain operates. (Lata et al., 2019)
However, the requisite and complete data required to perform this training is rare, and any attempt to create them manually even more complicated. As such, it became necessary to augment the available data automatically. This necessity is where GANs come in. They automatically create realistic datasets from minimal existing data that can, in turn, equip machines with the requisite data for active learning. (Lata et al., 2019)
The utilization of GANs for data augmentation provides a plethora of benefits thanks to GANs capacity to produce top quality data while also being suitable for diverse scenarios. However, it is not without its disadvantages. GANs are unable to effectively and quickly generate discrete data such as text. Also, they usually require significant processing power, they are quite challenging to train, and they may be unstable or overfit the actual data. (Tanka & Aranha, 2019)
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