From face ID on iPhone X to stock-monitoring robots in Walmart, artificial intelligence (AI) is pervasive. One of AI’s most impressive feats is its ability to create original visuals.
But, the power to create comes with the responsibility of ensuring copyright laws are being upheld. While many tools offer guidelines, it’s impossible to gatekeep every user.
Generative Adversarial Networks (GANs)
GANs have been a game-changer in image generation, allowing AI algorithms to generate realistic data. They do this by training two neural networks against each other: a generator and a discriminator. The generator produces new data, while the discriminator tries to identify if it is real or fake. As they train against each other, the generator becomes more and more realistic.
The generator is typically seeded with a random sample from a distribution such as multivariate normal, while the discriminator is trained on known images and tries to guess whether an image is real or generated. This is achieved by presenting it with a series of fake and real images, which it evaluates using the cross-entropy loss function.
This approach contrasts with traditional discriminative learning, which maps features to a label (for example, a picture of a cat or a currency note). Ian Goodfellow, the inventor of GANs, likens the two processes to counterfeiters and cops in a game of “cat and mouse” where the counterfeiter learns how to pass false bills and the cop learns how to catch them.
Stable Diffusion Networks (SDNs)
Unlike GANs, which pit two neural networks against each other, stable diffusion models are not adversarial. Instead, they learn to denoise random noise and eventually reach a desired sample, such as an image.
This is accomplished by training a U-Net model to predict the noise that will be added to each iteration of the process. This is what makes Stable Diffusion so much faster than previous methods like DALL-E and Google’s Imagen.
In order to use an AI image generator, the user will first enter a description into a text box. This description may be a general idea or an explicit image prompt, such as “a cat”. The platform will then add random noise to the image and iterate until it produces an image that matches the prompt. The resulting image can then be tweaked or edited by the user. This process allows for rapid prototyping and iteration. It also reduces the cost of producing an image and increases accessibility for new forms of art.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a subset of artificial intelligence that focuses on human language. It is used for tasks such as text classification and sentiment analysis.
NLP is often combined with computer vision (CV) to generate images and videos. CV is a set of algorithms that identify objects and their location in an image.
In recent years, a number of different models have been developed to generate image captions. The most popular approach uses an encoder-decoder framework with convolutional neural networks to encode visual features and recurrent neural networks to generate text descriptions.
These models are able to generate more realistic text and image content, making them more useful than previous methods. They can be used in a variety of fields, including medical imaging. However, they have also been used in the arts for image synthesis and art creation. These models can be used to create artwork, write blog posts, and even create videos.
Deep learning is the term used to describe a computer architecture that learns progressively and automatically without any explicit instructions. It is a mathematical manipulation that mimics the way neurons in the human brain represent information both at the single-unit and neural populations level.
The most popular and powerful deep learning algorithms are artificial neural networks (ANNs). These computer programs mimic the biological neural network that constitutes animal brains. They allow computers to perform tasks that would be difficult or impossible to express using rule-based programming.
GANs are a type of ANN that use an adversarial training process to generate synthetic images. They consist of two neural networks: a generator and a discriminator. The generator tries to produce images that fool the discriminator, and the discriminator tries to distinguish real samples from generated ones. The generator and the discriminator optimize different objective functions, pushing against each other in a zero-sum game. GANs have many applications including image synthesis and text-to-image translation.