Artificial Intelligence can generate images with the help of a Generative Adversarial Network.
Introduction
Artificial Intelligence is the advanced intelligence reflected by computers, which can reflect the cognitive thinking and decision-making skills that humans possess. Using this advanced intelligence, computers can perform actions like that of humans.
This advanced intelligence of the computers can learn using the experiences and use this learning to perform tasks just as that of humans. To achieve this, computers use the application of artificial intelligence known as Machine Learning. Using Machine Learning, computers can learn and take actions using what has been learned, and to do these concrete programs are not required. There are specifically four types of learning, namely:
· Supervised Learning
· Unsupervised Learning
· Semi-supervised Learning
· Reinforced Learning
Supervised Learning
In supervised learning, computers are capable of taking actions based on what has been learned based on examples processed. This learning is then used to work on upcoming cases. The computer process examples and store the results. This result is then analyzed when new cases are treated.
Supervised learning algorithms take data labeled as correct and use this data to make decisions. Thus supervised learning algorithms make decisions based on predetermined results. The algorithm compares the obtained output with that of the correct output. Any deviation leads to modification in the supervised learning algorithm. The supervised learning algorithm is categorized into Classification and Regression.
Unsupervised Learning
Supervised learning algorithm work on patterns labeled as right answers. For a supervised learning algorithm to work on it, it must identify the correct answers and use them to make decisions. If it is impossible to identify the correct answer, supervised learning algorithms are fruitful. To overcome this, unsupervised learning needs to be taken.
Unsupervised learning algorithms are used when the correct answer is unobtainable or impossible to achieve. Unsupervised learning algorithms are used when the dataset does contain correct answer labels. Unsupervised learning algorithms process data that does not contain any type of label.
The objective of the unsupervised learning algorithms is to find the pattern that exists in the dataset under process. Unsupervised learning algorithms are more fruitful when implemented on transaction databases.
Semi-Supervised Learning
When the dataset on which the algorithm is applied is incomplete or inaccurate, semi-supervised learning algorithms are used. The semi-supervised learning algorithm can work on the labeled dataset and unlabeled dataset. With a semi-supervised learning algorithm, voluminous unlabeled data is processed. As a result, the cost and time taken to process the unlabeled data are less than the time and cost taken by the supervised or unsupervised learning algorithms.
Reinforcement learning
The reinforcement learning algorithm works in a trial and error pattern. First, the algorithm tries to find the correct solution, and if the incorrect result is obtained, the algorithm bears a penalty. Therefore, the algorithm’s objective is to maximize the correct solution and minimize the penalty for the incorrect solution.
Generative Adversarial Networks
Generative Adversarial Networks use semi-supervised learning algorithms and unsupervised learning algorithms. This technique is used to process multi-dimensional data. The generative adversarial network contains a pair of a network trained against each other.
One network is trained as an art form in this pair, and the other is trained as an art expert. The art forger network acts as a generator and is responsible for making forgeries and creating realistic images. On the other hand, an expert network aims to discriminate the forged and realistic images and different forge and realistic images.
The forged network is designated by the alphabet Gi, and the expert network is designated by the alphabet Dj. The forge network named generator cannot access real images but it interacts with the expert network named discriminator to gain learning related to real images. The expert network (the discriminator) links sample images and real images.
The forge network generates forged images based on learning from the expert network (the discriminator). Suppose the expert network experiences an error signal when accessing the image that belongs to the entire stack or forges stack. In that case, this error signal is transferred to the forge network (the generator). The forge network then uses this learning to produce forge images of high quality.
The forge and generator networks are implemented in the form of connected layers. The forge network (the generator) and the expert network (the discriminator) are differentiated.
The forge network (the generator network) generates an image from the latent space and maps it to the image space. For example, the following equation can represent this relation between forge network and expert network:
Here, z belongs to latent space, and x is an image.
In the Generative Adversarial Networks (GANs), the expert network (the discriminator network), Dj ,
Conclusion
Artificial Intelligence is moving at a rapid pace. Due to the evolutionary approach of artificial intelligence algorithms, it is being used to generate images. With this evolutionary approach, an algorithm such as Generative Adversarial Networks (GAN) came into existence.
The GAN network is used to generate images. The GAN network consists of two networks that work against each other and use probability distribution to generate images. Based on this probability distribution, realistic images are developed.
The GAN has Generator and Discriminator network. The Generator network looks for a pattern in the dataset and uses the identified pattern to generate images. The Discriminator network authenticates the generated images. The discriminator network ensures that the generated image is one from the training set. It can be said that the discriminator network checks whether the generated image is a real image or the generated image is a fake image.
The generator network, also called the forge network, generates fake and forged images. The forge image is generated using bifurcation and chaos and continuation and branch switching techniques. The forge network also uses the stability and sensitivity method to generate forge images. The objective of the forge network is to generate an image that is not detected by the expert network (the discriminator).
The objective of the expert network (Discriminator Network) is to process the forged image generated by the forge network (the Generator). The expert network uses a binary classifier to judge whether the image is forged or not. This is done by comparing the real image with the image generated by the forge network.
The comparison results tell whether the image belongs to the same multi-dimensional data distribution. If the image does not belong to the same multi-dimensional data distribution t, it reflects the deviation in the data distribution.
To implement the GAN network, the TensorFlow library is used. This article begins with describing learning algorithms used to artificial intelligence and digs deep into the GAN. Then, this article discussed the Expert network and Discriminator network used to generate images, and it provided the importance and mechanism used to generate images.
The domain of artificial intelligence evolving and its use in generating images is also evolving. The GAN networks are being used rapidly and generating images at an amazing pace. This article provides the theoretical aspect of the use of GAN to generate images.
