The basic principles of generative models with an example

Generative models are a class of machine learning models that are designed to generate new data that is similar to the training data they were trained on. These models learn the underlying probability distribution of the training data and use it to generate new samples that are similar to the original data.

One example of a generative model is the Generative Adversarial Network (GAN). A GAN consists of two neural networks: a generator and a discriminator. The generator generates new data samples by randomly generating a noise vector and using it to generate new samples. The discriminator, on the other hand, tries to distinguish between the real data samples and the ones generated by the generator.

During training, the generator tries to generate samples that are similar to the real data to fool the discriminator. Meanwhile, the discriminator tries to correctly classify whether a given sample is real or generated. As the training progresses, the generator learns to generate more realistic samples that can fool the discriminator, and the discriminator becomes more accurate in distinguishing between real and generated samples.

Once the training is complete, the generator can be used to generate new data samples that are similar to the training data. For example, a GAN can be trained on a dataset of images of faces and then be used to generate new images of faces that look similar to the original ones.

Generative models have a wide range of applications, such as image and video generation, text generation, and music generation. They can also be used for data augmentation, which involves generating new samples to augment a dataset and improve the performance of a machine learning model.


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