Generative adversarial networks (GANs) are a type of deep learning architecture that uses two competing neural networks to generate new data. These two networks, the generator and the discriminator, train against each other, helping to produce a more accurate output. GANs can be useful in various fields, including computer vision, robotics, image generation, video synthesis, and natural language processing.
The best way to understand how GANs work is through an analogy: a competition between an art forger (the generator) and an art critic (the discriminator).
This adversarial "game" continues, with both networks getting progressively smarter. Eventually, the forger becomes so skilled that the critic can no longer reliably tell the difference. At this point, the GAN is trained and can generate new, highly realistic data.
Both convolutional neural networks (CNNs) and generative adversarial networks (GANs) are deep learning architectures, but they have distinct strengths and applications. CNNs are often used for image classification and object detection tasks, while GANs are generally designed for generating new data instances.
Feature | CNNs | GANs |
Data usage | Mostly labeled datasets | Labeled or unlabeled datasets |
Output | Classification, feature extraction | Diverse, new data instances |
Model type | Discriminative | Generative |
Primary tasks | Image classification, object recognition | Image generation, data augmentation, synthetic data creation |
Feature
CNNs
GANs
Data usage
Mostly labeled datasets
Labeled or unlabeled datasets
Output
Classification, feature extraction
Diverse, new data instances
Model type
Discriminative
Generative
Primary tasks
Image classification, object recognition
Image generation, data augmentation, synthetic data creation
While all GANs share the generator-discriminator structure, different variations have been developed to solve specific problems. Here are a few of the most important types:
While the fundamental concept of using two adversarial networks remains consistent across generative adversarial network variations, researchers have explored a variety of architectural and training modifications to address limitations and improve performance for specific applications.
GANs have unlocked new possibilities across many industries. Their applications generally fall into these key areas:
This is the most famous application of GANs. It includes generating realistic images of people, places, and objects; creating digital art and music; and enabling powerful image editing tools like style transfer (making a photo look like a painting), super-resolution (sharpening blurry images), and text-to-image synthesis.
High-quality data is the fuel for machine learning, but it can be rare, expensive, or private. GANs help solve this by generating synthetic data. In healthcare, GANs can create realistic but anonymous medical scans to train diagnostic models without violating patient privacy. In finance, they can generate synthetic transaction data to train better fraud detection systems. This helps overcome data scarcity and balance datasets.
GANs can learn the patterns in complex systems to create realistic simulations. This is used to generate diverse scenarios for training self-driving cars, predicting the next frames in a video, or even discovering potential molecular structures in drug discovery.
By training a GAN on "normal" data, it becomes very good at spotting anything that doesn't fit the pattern. This is used for detecting fraudulent financial activity, identifying network intrusions in cybersecurity, and finding defects in manufacturing.
Developing and deploying GANs requires significant computational power and a robust MLOps platform. Google Cloud offers the tools to support the entire workflow:
Start building on Google Cloud with $300 in free credits and 20+ always free products.