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Business License Cantonese No.  Information Technology Co., Ltd. Everyone is a Product Manager Homepage Training Course Classification Browse Activities Lectures Q&A Corporate Training Fishing News Search Registration Login Generate Adversarial Network N "Volume of left and right fighting Wang I Xiaodangjia attention - Comments Browse and collect Two major problems for minute-end product managers How to analyze and design products from multiple perspectives such as market users and business How to effectively manage and promote project implementation The above introduces the basic concept of recurrent neural network RNN Today Let’s introduce the generative adversarial network N. The generative adversarial network N is a very interesting deep learning algorithm that is widely used in I face-changing, style Rich People Phone Number List transfer and other scenarios. 1. Basic principles The basic principle of the generative adversarial network N is to use two neural networks That is, the generator enerr and the discriminator iriinr compete against each other to learn. The generator tries to generate data that is as real as possible to deceive the discriminator, while the discriminator tries to distinguish real data and generated data as accurately as possible. Generator workflow



The process receives a random noise and generates data from this noise. This process can be viewed as randomly sampling from a latent space and then mapping it to the data space. The goal of the generator is to find such a mapping that the generated data is as close as possible to the real data distribution. The discriminator workflow receives an input, which may be real data or generated data. The discriminator needs to output the probability that the input data is real data. The goal of the discriminator is to maximize its classification accuracy on real and generated data. The generator and discriminator are optimized alternately during the training process. First, the generator is fixed and the discriminator is optimized to distinguish real data from generated data as accurately as possible. Then fix the judgment