yraining process of N is more

 complicated and requires more hyperparameters to be adjusted. For example, the learning rate of the generator and discriminator, the choice of optimizer, the choice of noise distribution, etc. will all affect the training effect of N. Mode Collapse Problem N may suffer from mode collapse e lle problem where the generator always generates the same image. This is because during the training process, the generator may find a "shortcut" that can fool the discriminator. It only generates a certain type of images and ignores other images. This makes the generated images lack diversity. Training process of training stability problem

 N The abilities of the generator and the discriminator need to be synchronized as much as possible. If the ability of the discriminator is too strong, the generator may not be able to find a suitable Rich People Phone Number List direction for optimization. On the contrary, if the ability of the generator is too strong, the discriminator may be deceived and cannot correctly guide the training of the generator. . This instability makes the training process of N require very careful selection and adjustment of hyperparameters. Long training time Since N contains two neural networks and needs to be trained alternately, the training time of N is usually long. It is difficult to

quantitatively evaluate the results generated by N Data quality is difficult to quantitatively assess. Although it can be assessed manually, this method is highly subjective and inefficient. Although there are some quantitative assessment methods such as Inein re, FI, etc., these methods have their own limitations. Generation process of black box problem N It is a black box process that is difficult to understand and explain. This may become a problem in some applications that require interpretability. 4. Summary This article introduces the basic principles and application scenarios of the generative adversarial network N. It controls the generator and the discriminator by letting the generator and the discriminator control Hubao finally produced a higher-quality generator and discriminator. In the next article, we will introduce rnfrer, which is widely used in large language models, so stay tuned. This article was originally published by I Xiaodangjia on