What is a Generative Adversarial Network?
Generative adversarial networks (GANs) are deep learning-based generative models designed like a human brain — called neural networks. These neural networks are designed to identify and learn patterns or regularities in a dataset, which can be used to create new results that are nearly impossible to distinguish from the original dataset. This is a neural network with an imagination.
GANs make use of two sub-models which include a generator that creates new data instances and a discriminator that analyzes instances for legitimacy.
Think of it like a counterfeiter (generator) and a detective (discriminator) both trying to outsmart the other. The more the counterfeiter passes fake dollar bills, the smarter the detective becomes at identifying them. The counterfeiter needs to get better at making fakes, and the detective needs to learn the new tricks being employed in the counterfeits.
GANs work by being fed training data, then, the “adversarial” part kicks in as the generator and discriminator learn from each other. The aim of these models is for the discriminator to differentiate between false and authentic data. Both data types work together to train the model that’s being used to evaluate and make decisions based on complex data like audio, video, or image files.
The Origin of GANs
The Generative Adversarial Network concept was born from an argument at a bar between Ian Goodfellow of the University of Montreal and his friends. In a special Quora discussion about GANs, Facebook’s AI research director Yann LeCun termed adversarial training “the most interesting topic in the last 10 years in machine learning.”
Benefits of a Generative Adversarial Network
- Data labelling is a costly operation. Because GANs are unsupervised, they do not require labelled data to be trained, which can greatly reduce the cost of the project.
- Currently, GANs provide the sharpest image quality. Adversarial training enables this, making them one of the most effective generative models.
- GANs take the initiative to make categories for data instead of a human needing to assign the parameters, requiring less manual work for data scientists.
Potential Problems With the GAN Model
While there are a considerable amount of benefits, a GAN can suffer from major problems, including:
- Non-Convergence: This occurs when the model parameters fluctuate, become unstable, and never converge.
- Mode Collapse: If the generator collapses, the model will produce a limited variety of samples, rendering it ineffective.
- Diminished Gradient: If the discriminator becomes too effective, the generating gradient disappears, and consequently doesn’t learn anything.
What Sets GANs Apart?
Despite potential roadblocks, there are still many factors that make GANs a compelling model. Various machine learning models are highly effective in taking away a lot of the human element and costs of crunching, processing, and deciphering data. What makes a GAN different is its efficiency. For instance, even if your training data is insufficient, a GAN can learn from your existing dataset and create realistic, albeit fake, images that can greatly augment your dataset.
It also finds errors and anomalies humans can’t. By design, it’s an unsupervised learning process, but it can be used in reinforcement, semi-supervised, and multi-modal learning.
Creator Ian Goodfellow explained it this way as he talked about data being a big bottleneck for progress:
“For example, if we want to use neural networks for medicine, we might need to get access to lots and lots of patient records before we can build an accurate model. But if we could use something like GANs to reduce the number of records that we need, then we would only need to get a few patient records, then we can have the whole system up and running much faster without needing to accumulate nearly as many records.”
Popular Use: Generative Adversarial Network in Visuals
Because of their capacity to interpret and replicate visual material with increasing precision, GANs are becoming a preferred ML model for multiple use cases:
- Super-Resolution Images: A GAN can fix defects in a low-resolution image and complete details of the object using the text description, resulting in a high-quality lifelike image.
- Art Creation: Based on the previous work of an artist, or solely using a text description, a GAN can construct realistic artwork that matches the original dataset.
- Image Conversion: Convert images of one type into another. For example, sketches can be converted into photographs.
- Image Restoration: GANs can fill in the gaps for images that have missing sections.
- Text-to-Image and Image-to-Text: A realistic image can be created using a text description and vice versa.
- Deep Fakes: Create a computer-generated face to replace one person’s face in an image or video.
Practical Business Applications of GANs
Generative Adversarial Networks could be the future of security, healthcare, and transportation. The possibilities of adversarial learned data outputs are immense.
Medical image analysis is a key application of GANs, which is projected to significantly decrease doctor effort and contribute to more sustainable health systems. For example, GANs are highly effective at generating realistic-looking medical images by FID standards. A study published in IEEE experimented with GAN-generated synthetic images to train a CNN classification model for tissue recognition. The tissue recognition accuracy achieved by the trials with the synthetic images was 98.83%, demonstrating the usefulness and applicability of synthesizing medical images using a GAN.
There are multiple applications of GANs in the transportation industry. For instance, components are visually examined for flaws during airplane maintenance. There is no photographic evidence of the flaws. Images generated by the GAN are used to evaluate components for faults to improve diagnostic data quality, digitize it more, and speed up the process. GANs are similarly utilized to build a functioning airfoil inverse design. In this context, the GAN model is designed to overcome the issues associated with form parameterization in traditional approaches and to find and exploit patterns in data at a lower level of abstraction.
Images are manipulated by hackers by inserting dangerous data into them. This deceives the neural network and jeopardizes the algorithm’s intended operation. As a result, undesired information may be revealed and compromised.
Such instances of fraud can be detected using generative adversarial networks. They can be used to improve the robustness of deep learning models. The neural network can be trained to detect any dangerous information that hackers may add to photos. Researchers and analysts purposefully construct bogus instances and use them to train the neural network. As it analyses several photos, the network improves.
Using GANs Effectively
GANs have enormous potential for both good and bad since they can learn to replicate any data distribution. Goodfellow punctuated this point by saying,
“Depending on which way fake data is being used, it could give an advantage to either the attacker or the defender.”
It can be trained to generate worlds that are disturbingly similar to our own in any domain: visuals, music, speech, and literature. They can, however, be utilized to create large volumes of believable fake media in the blink of an eye. It’s crucial to have a strong understanding of this technology and learn how it works to make sure it’s used optimally and effectively.
RapidMiner users can develop GANs using Python code, then repackage them as operators within visual drag-and-drop workflows. Using RapidMiner, anyone within your organization can leverage GANs to train generative models and augment data when it’s called for.
Request an enterprise demo of RapidMiner today for a hands-on look at how GANs and other machine learning models can be implemented to drive real impact across your organization.