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Generative Adversarial Network (GAN)
A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.
Is GAN deep learning or machine learning?
Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are model architecture for training a generative model, and it is most common to use deep learning models in this architecture.
What is a GAN used for?
GAN can be used to detect glaucomatous images helping the early diagnosis which is essential to avoid partial or total loss of vision.
Why GANs?
If your training data is insufficient, no problem. GANs can learn about your data and generate synthetic images that augment your dataset.
Can create images that look like photographs of human faces, even though the faces don’t belong to any real person from the given distribution. Isn’t that incredible?
Generate images from descriptions (text to image synthesis).
Improve the resolution of a video that captures finer details (low-resolution to high-resolution).
Even in the audio domain, GANs can be used to produce synthetic, high-fidelity audio or perform voice translations.
Overview of GAN Structure
A generative adversarial network (GAN) has two parts:
The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.
The discriminator learns to distinguish the generator’s fake data from real data. The discriminator penalizes the generator for producing implausible results.
When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it’s fake:
As training progresses, the generator gets closer to producing output that can fool the discriminator:
Finally, if generator training goes well, the discriminator gets worse at telling the difference between real and fake. It starts to classify fake data as real, and its accuracy decreases.
Both the generator and the discriminator are neural networks. The generator output is connected directly to the discriminator input. Through backpropagation, the discriminator’s classification provides a signal that the generator uses to update its weights.
Advantages of GANs over Other Generative Models
GANs today dominate over all other generative models. Let’s see why:
Data labelling is an expensive task. GANs are unsupervised, so no labelled data is required to train them.
GANs currently generate the sharpest images. Adversarial training makes this possible. Blurry images produced by Mean Squared Error stand no chance before a GAN.
Both the networks in GAN can be trained using only backpropagation.