MOCK DATA GENERATOR USING GENERATIVE ADVERSARIAL NETWORKS

摘要

Mock test data is generated by providing a random input to a generator model. The random input is transformed into generated data that is then provided to a discriminator model along with production data. The discriminator model classifies the generated data and the production data as either fake or real. The discriminator model is trained by updating weights through backpropagation. Similarly, the generator model is trained to provide adjusted generated data. When the discriminator model is unable to distinguish between the classified real data and the adjusted generated data, the generator model is used to generate mock data for an application being tested.