Generative Adversarial Networks (GANs) are a type of machine learning model used for generating new data samples that resemble a given dataset. A GAN consists of two neural networks: a generator and a discriminator. The generator creates new data points, while the discriminator evaluates them against real data, determining whether they are fake or genuine. During training, these two networks compete against each other. The generator improves its output to fool the discriminator, while the discriminator becomes better at distinguishing real from fake data. This process continues until the generator produces high-quality data that is close enough to the real samples.
In the context of data augmentation, GANs can significantly enhance the training datasets for machine learning models. For example, in image classification tasks where collecting more labeled data is expensive or time-consuming, GANs can create synthetic images based on existing ones. Suppose you have a small set of images of cats and dogs. By training a GAN on these images, you can generate new images that maintain the same characteristics but are entirely new. This approach helps in improving the diversity of training data without the need to collect real-world data, which can save time and resources.
Moreover, using GANs for data augmentation can help address the class imbalance problem. For instance, if one class in your dataset has significantly fewer samples than another (like images of rare diseases), a GAN can be trained specifically on that underrepresented class to generate more examples. This additional synthetic data helps the machine learning model to learn better, as it gets a more balanced view of the different classes, ultimately leading to improved model performance.