So optimize D Parameters of θ d \theta_d θ d You need to maximize the entire expression. If the classifier is not perfect, it will make mistakes, Then the whole is a negative number. Then map to the real sample x x xd Data distribution of p d a t a ( x ) p_ x ˉ, Then input to the classifier D in, The classifier should be classified into 0, Then the whole is 0. GAN Build models, In short, With a simple noise distribution p z ( z ) p_z(z) p z ( z ) Sampling generates some noise z z z. therefore goodfellow Then we will bypass this difficulty, Say to learn a data distribution, The effect is about the same. The reason is to generate data, Need to fit the data distribution of the sample, It is difficult to calculate when maximizing the likelihood function, And as the dimension increases, the amount of calculation explodes. Goodfellow Provide GAN Source code GAN Generated motivationġ4 year goodfellow Doing it GAN In this job, I think deep learning is doing a good job in identifying models, But not in the generation model. Learn from Li Mu AI- Paper precision series -GAN- Beep station video connection Specifically, I learned the video of Teacher Li Mu, I have studied it carefully for several times. GAN It must be the most representative work in the field of in-depth learning in the past five years. From the motivation of the thesis, Algorithm content, experimental result, Environment building, Code runs, The code interpretation is explained systematically. Then I spent a day and a night python I have learned all the knowledge of object-oriented programming. However EMI only release The algorithm itself, There is no script to solve the test set. GMOEA It's the Cheng ran teacher group of South University of science and Technology 2021 Years published in IEEE Trans on Cybernetics The paper of, The main contribution is to GAN Applied to MOP In multi-objective evolution. Replace the project image source and installation package
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