Body: <p>Can I refine StyleGAN or StyleGAN2 without retraining it for many days, such that its pretrained model is trained to generate only faces similar to a (rather small) set of reference images?</p>
  <p>I would like to avoid creating a large dataset and training for many days to weeks, but use the existing model and just bias it towards a set of images.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-21T10:21:01.287Z
Comments.Text: Are you asking if it's possible to apply transfer learning to StyleGAN?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-21T12:43:39.870Z
Comments.Text: I think transfer learning means solving another problem. My problem at hand is that I would like to use stylegan to generate images of people that are similar to a given input set (that may or may not be good represented in the pre-trained model). I hope it is possible to use the pre-trained model and train it on a new set without losing too much of the pre-trained weights.
Comments.UserDisplayName: allo
Tags: <generative-adversarial-networks><pretrained-models>
Title: Can StyleGAN be refined without a full training?
UserDisplayName: allo
ThreadText: # Can StyleGAN be refined without a full training?
  
  (Asked by: username_0 on 2020-06-20)
  
  Can I refine StyleGAN or StyleGAN2 without retraining it for many days, such that its pretrained model is trained to generate only faces similar to a (rather small) set of reference images?
  
  I would like to avoid creating a large dataset and training for many days to weeks, but use the existing model and just bias it towards a set of images.
  
  **Tags:** generative-adversarial-networks, pretrained-models
  > **Comments:**
  > **username_1**: Are you asking if it's possible to apply transfer learning to StyleGAN?  
  > **username_0**: I think transfer learning means solving another problem. My problem at hand is that I would like to use stylegan to generate images of people that are similar to a given input set (that may or may not be good represented in the pre-trained model). I hope it is possible to use the pre-trained model and train it on a new set without losing too much of the pre-trained weights.  
  
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  ## 0 answers
  
  