Artificial intelligence has come a long way in recent years, and one area where it’s become especially innovative is text-to-image generation. This innovation allows anyone to produce digital images that resemble masterful works of art by simply feeding textual prompts into an opaque system.
The latest iteration of this technology, Google AI’s Imagen, uses large transformer language models and image diffusion models to achieve a high degree of photorealism.
Generative Adversarial Networks (GANs)
GANs are a type of deep neural network that uses generative modeling to generate realistic images. They consist of a generator and discriminator. The generator generates a replica distribution of the target and the discriminator attempts to distinguish between real and fake samples. The generator then improves its fake samples to better match the target distribution. GANs have achieved impressive results in recent years. For example, Nvidia’s StyleGAN can generate high-resolution head shots of fictional characters, while Microsoft researchers recently released ObjGAN, which can understand captions and sketch layouts based on text descriptions.
However, despite the rapid progress in GAN technology, some limitations remain. For one, many GANs suffer from training instability, which can lead to problems like non-convergence and mode collapse. In addition, the current loss function of most GANs can be optimized to minimize the Inception score (IS) or Frechet Inception Distance (FID). This has resulted in a reduction in diversity of generator outputs.
Convolutional Neural Networks (CNNs)
Convolutional neural networks (CNNs) are a popular image recognition model. They are inspired by the layered structure of visual neurons in the human brain. In a CNN, the input image is abstracted into feature maps that identify key features such as edges, corners, or combinations of edges.
Then a series of convolutional and pooling layers are stacked to extract progressively complicated characteristics. This output is then flattened into a one-dimensional vector that represents the final output, such as a categorical label like cat or dog.
Unlike traditional neural networks, CNNs use shared weights and bias values across all hidden neurons in a layer. This reduces the number of parameters that need to be learned and makes training faster. Also, they have been shown to be more general than previous models in terms of the way that they can recognize patterns in images. This translates to better performance when applied to new tasks.
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are a variant of neural networks that work better when the data is sequential like Time-Series or text data. They use a memory state to keep track of previous inputs and generate outputs informed by them.
However, RNNs are prone to the vanishing gradient and exploding gradient problems which make training them more difficult. Additionally, they are inherently sequential and can be computationally expensive to parallelize which limits their scalability.
LSTM networks solve these issues by introducing a specialized memory cell that can selectively retain or forget information over time. These cells are controlled by three gates: the input gate, the forget gate and the output gate. The input gate regulates new data flow into the memory cell, the forget gate regulates whether or not information in the memory cell is forgotten and the output gate regulates which outputs are passed out of the model. EXAMM has evolved a wide variety of these memory structures.
Unlike traditional machine learning models that are trained on specific datasets, deep learning algorithms can learn from unlabeled data. This means that they can identify patterns and features that humans cannot. For example, a computer program can be shown images of dogs and recognize them by learning the characteristics that all dogs share. This process is called feature extraction. It would take a software engineer weeks to select the correct features for such a task, but an ANN can do it for itself.
This capability has revolutionized many applications. It is enabling automakers to develop self-driving cars, for example. Moreover, industrial automation is using deep learning to detect issues with products like minor product defects and prevent expensive recalls. In addition, medical researchers are able to analyze more complex images and find the best way to treat cancer cells. This is all possible because of the availability of large amounts of data and superior computational power.