Music generation has been established as a prominent topic in artificial intelligence and machine learning over recent years. In most recent works on RNN-based neural network methods have been applied for sequence generation. In contrast, generative adversarial networks (GANs) and their counterparts have been explored by very few researchersfor music generation. In this paper, a classical system was employed alongside a new system to generate creative music. Both systems were designed based on adversarial networks to generate music by learning from examples. The classical system was trained to learn a set of music pieces without differentiating between classes, whereas the new system was trained to learn the different composers and their styles to generate a creative music piece by deviating from the learned composers' styles. The base structure utilized was generative adversarial networks (GANs), which are capable of generating novel outputs given a set of inputs to learn from and mimic their distribution. It has been shown in previous work that GANs are limited in their original design with respect to creative outputs. Building on the Creative Adversarial Networks (CAN) , this work applied them in the music domain rather than the visual art domain. Additionally, unrolled CAN was introduced to prevent mode collapse. Experiments were conducted on both GAN and CAN for generating music, and their capabilities were measured in terms of deviation from the input set.

Convolutional neural networks (CNNs) have been combined with generative adversarial networks (GANs) to create deep convolutional generative adversarial networks (DCGANs) with great success. DCGANs have been used for generating images and videos from creative domains such as fashion design and painting. A common critique of the use of DCGANs in creative applications is that they are limited in their ability to generate creative products because the generator simply learns to copy the training distribution. We explore an extension of DCGANs, creative adversarial networks (CANs). Using CANs, we generate novel, creative portraits, using the WikiArt dataset to train the network. Moreover, we introduce our extension of CANs, conditional creative adversarial networks (CCANs), and demonstrate their potential to generate creative portraits conditioned on a style label. We argue that generating products that are conditioned, or inspired, on a style label closely emulates real creative processes in which humans produce imaginative work that is still rooted in previous styles.

Artificial Intelligence has surely stormed mankind in past years, the machines are extremely good at imitating what we tell them to do. But AI and creativity are counterparts, creativity is the abstract concept that is still missing from the core AI field. For the past few years, researchers have been trying to decode the machine's ability to mimic human-level intelligence to generate creative products such as jokes, poetry, problems, paintings, music, etc. The integral aim is to show that AI algorithms are in-fact intelligent enough to produce art without involving human artists but taking into account the human creative products in the learning process. Several interesting algorithms like GANs(Generative Adversarial Networks) have been introduced to explore the creative space.

Artificial intelligence (AI) pioneer Alan Turing infamously asked many years ago, "Can machines think?" Today, Oxford mathematician Marcus du Sautoy poses an equally provocative question: Can machines create? In his new book, "The Creativity Code: Art and Innovation in the Age of AI," du Sautoy explores the structural nature of creativity in human endeavors, and looks at areas where AI can have the most influence. "Creativity is a code that evolution across millions of years has honed inside our brains," du Sautoy writes. "Is our creativity in fact more algorithmic and rule-based than we might want to acknowledge? Can we hope to crack the creativity code?"

Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model. Rutgers comp sci prof Ahmed Elgammal runs an Art and AI Lab where they use "Creative Adversarial Networks" to produce new artworks: CANs use a "discerner" that seeks out "novelty," not fidelity to the statistical predictions of the model. The underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set." Elgammal recent exhibited a show called Faceless Portraits Transcending Time at Chelsea's HG Contemporary gallery; and his choice of portraiture as a means of showcasing the capabilities of CANs has proven to be controversial: as art historian John Sharp says, "You can't really pick a form of painting that's more charged with cultural meaning than portraiture." Portraits use extensive, coded symbology to say something about their subjects, and CANs do not, by themselves, understand or correctly use these symbols in the works they create.

Lately, GANs (Generative Adversarial Networks) have been really successful in creating interesting content that are fairly abstract and hard to create procedurally. This paper, aptly named CAN (Creative, instead of Generative, Adversarial Networks) explores the possibility of machine generated creative content. This article assumes familiarity with neural networks, and essential aspects of them, including Loss Functions and Convolutions. I will follow the paper's structure as much as I can. I will add my own bits to help better understand the material.

Creativity is one of the great challenges for machine intelligence. There is no shortage of evidence showing how machines can match and even outperform humans in vast areas of endeavor, such as face and object recognition, doodling, image synthesis, language translation, a vast variety of games such as chess and Go, and so on. But when it comes to creativity, the machines lag well behind. For example, machines have learned to recognize artistic style, separate it from the content of an image, and then apply it to other images. That makes it possible to convert any photograph into the style of Van Gogh's Starry Night, for instance.

Scientists have developed an AI artist whose masterpieces could pass off as human-made. The system builds upon earlier techniques to generate art and learn about style through observation, but unlike earlier approaches, the new network also has the ability to become creative. When put to the test, the researchers found that humans could not tell the difference between those created by the system and artwork made by contemporary human artists – and sometimes, the AI-generated images even scored higher. The system builds upon earlier techniques to generate art and learn about style through observation, but unlike earlier approaches, the new network also has the ability to become creative. Like the GAN system, the Creative Adversarial Network (CAN) also uses two sub-networks.

GAN's consists of two competing neural networks, namely the Generator and the Discriminator. As is suggestive of the name, the Generator is responsible for generating data from some input (this input can be noise or even some other data). The discriminator is then responsible for analysing that data and discriminating wether that data was real(if it came from our dataset) or if its fake(if it came from the generator). If the above equation was too complex for you, you are not alone. I will go through this equation step by step and explain what each component means.