{'fraction_non_alphanumeric': 0.01937046004842615, 'fraction_numerical': 0.004842615012106538, 'mean_word_length': 4.703448275862069, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Generative Adversarial Networks: Outline and its Use Cases

Generative adversarial networks (GANs) are the youngest neural networks which have been in the highlight for recent years now. GANs were first introduced by Ian Good fellow and his colleagues in 2014.This paper gives the brief description of Generative Adversarial Model, its architecture and various use cases where we can use these models. Some of the different models which were introduced are also mentioned. With the increasing use of digital technology, there is a lot of generation of data which may be labeled or may not be. Using these data we try to generate some samples that are similar to our dataset, I.e, each sample has some properties similar to original dataset. Since we are generating new samples from ground truth, it can be used in many fields.