Generative Adversarial Networks (GANs) and Variational Autoencoder (VAEs) are both types of deep learning models that are used to generate new data, such as images, from a given input dataset. However, they work in slightly different ways and are used for different purposes.
A GAN is composed of two neural networks: a generator and a discriminator. The generator takes a random noise as input and generates new data, such as images, that are intended to be similar to the input dataset. The discriminator then receives both the generated data and real data from the input dataset, and attempts to distinguish between the two. The goal of the generator is to produce data that the discriminator can’t tell apart from the real data, while the goal of the discriminator is to correctly identify which data is real and which is generated. Through this process, the generator and discriminator are trained to work together, and the generator becomes better at producing realistic data.
On the other hand, VAEs are generative models that are used to learn a probabilistic representation of a dataset. VAEs consist of an encoder, which maps the input data to a latent space, and a decoder, which maps the data from the latent space back to the original space. The encoder learns to extract features from the input data, and the decoder learns to generate new data from the features.
VAEs are trained to optimize the likelihood of the input data under a probabilistic model. Additionally, VAEs introduce a randomness in the process of decoding the input data, which allows them to generate new data that is similar to the input data.
In summary, GANs and VAEs are both types of deep learning models that are used to generate new data, such as images, but they work in slightly different ways. GANs are trained to produce realistic data using a generator and a discriminator, while VAEs are trained to learn a probabilistic representation of a dataset and generate new data from it.