Body: <p>Although the GAN is widely used due to its capability, there were generative models before the GAN which are based on probabilistic graphical models such as Bayesian networks, Markov networks, etc.</p>
  <p>It is now a well-known fact that GANs are excelling at image generation tasks. But I am not sure whether the generative models that were invented before GANs were used for image generation or not.</p>
  <p>Is it true that other generative models were used for image <strong>generation</strong> before the proposal of the GAN in 2014?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T07:15:32.717Z
Comments.Text: Could you clarify what you mean by "image generation" and whether you have any constraints on the type of model used? If the question is open enough then I would not be surprised to find automated image generation that predates deep neural networks, or any neural networks or even computers
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T09:27:00.763Z
Comments.Text: @NeilSlater I mean image generation using AI generative models  that exist before GAN.
Comments.UserDisplayName: hanugm
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Comments.CreationDate: 2021-08-07T11:58:03.780Z
Comments.Text: How about autoencoders?
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T13:21:04.483Z
Comments.Text: @user253751 It is identity function right? It may come under reconstruction rather than generation, but if it can able to generate diverse, then yeah.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-07T13:46:20.383Z
Comments.Text: An autoencoder is trying to simulate an identity function, but you can also delete the first half, inject random values into the bottleneck layer, and see what you get.
Comments.UserDisplayName: Criticize SE actions means ban
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Comments.CreationDate: 2021-08-07T13:46:59.140Z
Comments.Text: @user253751 Can it generate novel and realistic images?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-09T15:38:25.303Z
Comments.Text: @hanugm: Yes. In fact there is a minor change to autoencoders called variational autoencoders (VAEs) and they can be used in many similar situations as GANs, although they have different strengths and weaknesses. There is even a VAE-GAN architecture that combines both approaches
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-10T10:25:07.893Z
Comments.Text: You can also use any image classifier as a generator by optimizing the image to maximize the score of a particular class. This was called Deep Dream. In this case you need a starting image, which could be plain grey or black or white, but you can also start from other images.
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <generative-adversarial-networks><generative-model><history><image-generation>
Title: Is image generation not existent before generative adversarial networks?
UserDisplayName: hanugm
ThreadText: # Is image generation not existent before generative adversarial networks?
  
  (Asked by: username_0 on 2021-08-06)
  
  Although the GAN is widely used due to its capability, there were generative models before the GAN which are based on probabilistic graphical models such as Bayesian networks, Markov networks, etc.
  
  It is now a well-known fact that GANs are excelling at image generation tasks. But I am not sure whether the generative models that were invented before GANs were used for image generation or not.
  
  Is it true that other generative models were used for image **generation** before the proposal of the GAN in 2014?
  
  **Tags:** generative-adversarial-networks, generative-model, history, image-generation
  > **Comments:**
  > **username_1**: Could you clarify what you mean by "image generation" and whether you have any constraints on the type of model used? If the question is open enough then I would not be surprised to find automated image generation that predates deep neural networks, or any neural networks or even computers  
  > **username_0**: @NeilSlater I mean image generation using AI generative models  that exist before GAN.  
  > **username_2**: How about autoencoders?  
  > **username_0**: @user253751 It is identity function right? It may come under reconstruction rather than generation, but if it can able to generate diverse, then yeah.  
  > **username_2**: An autoencoder is trying to simulate an identity function, but you can also delete the first half, inject random values into the bottleneck layer, and see what you get.  
  > **username_0**: @user253751 Can it generate novel and realistic images?  
  > **username_1**: @hanugm: Yes. In fact there is a minor change to autoencoders called variational autoencoders (VAEs) and they can be used in many similar situations as GANs, although they have different strengths and weaknesses. There is even a VAE-GAN architecture that combines both approaches  
  > **username_2**: You can also use any image classifier as a generator by optimizing the image to maximize the score of a particular class. This was called Deep Dream. In this case you need a starting image, which could be plain grey or black or white, but you can also start from other images.  
  
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  ## 0 answers
  
  