Question: What models are used in the framework of GANs?
Evidence: Fortunately, we believe that the introduction of unsupervised generative language models presents a way in which to tackle this particular shortcoming of peer-to-peer markets. In 2014, Ian Goodfellow et. al proposed the general adversarial network (GAN) [5]. The group showcased how this generative model could learn to artificially replicate data patterns to an unprecedented realistic degree [5]. Since then, these models have shown tremendous potential in their ability to generate photo-realistic images and coherent text samples [5].
The framework that GANs use for generating new data points employs an end-to-end neural network comprised of two models: a generator and a discriminator [5]. The generator is tasked with replicating the data that is fed into the model, without ever being directly exposed to the real samples. Instead, this model learns to reproduce the general patterns of the input via its interaction with the discriminator.
The role of the discriminator, in turn, is to tell apart which data points are ‘real’ and which have been created by the generator. On each run through the model, the generator then adapts its constructed output so as to more effectively ‘trick’ the discriminator into not being able to distinguish the real from the generated data. The end-to-end nature of the model then forces both the generator and discriminator to learn in parallel [7]. While GAN models have shown great potential in their ability to generate realistic data samples, they are notoriously difficult to train. This difficulty arises from two-parts: 1) First, it is difficult to tune the hyper-parameters correctly for the adversarial model to continue learning throughout all of the training epochs [5]. Since both the discriminator and generator are updated via the same gradient, it is very common for the model to fall into a local minima before completing all of the defined training cycles. 2) GANs are computationally expensive to train, given that both models are updated on each cycle in parallel [5]. This compounds the difficulty of tuning the model’s parameters.
Nonetheless, GANs have continued to show their value particularly in the domain of text-generation. Of particular interest for our purposes, Radford et al. propose synthesizing images from text descriptions [3]. The group demonstrates how GANs can produce images that correspond to a user-defined text description. It thus seems feasible that by using a similar model, we can produce text samples that are conditioned upon a set of user-specified keywords.
We were similarly influenced by the work of Radford et. al, who argue for the importance of layer normalization and data-specific trained word embeddings for text generation [9] and sentiment analysis categorization. These findings lead us to question whether it is possible to employ recurrent neural networks with long short-term memory gates, as defined by Mikolov et al., to categorize product descriptions into categories based on the product's popularity [6].
The framework that GANs use for generating new data points employs an end-to-end neural network comprised of two models: a generator and a discriminator [5].  The generator is tasked with replicating the data that is fed into the model, without ever being directly exposed to the real samples. Instead, this model learns to reproduce the general patterns of the input via its interaction with the discriminator.
Answer: A generator and a discriminator.