Answers.Body: <p>The references you stated indeed are the right way to go regarding small dataset image synthesis.</p>
  <p>I'd research the space of few-shot image synthesis for what is out there, but something along the line of StyleGAN2 is a logical direction to look into.</p>
  <p>In addition, I'd like to state specifically that training a synthesizer on only 10-50 images is always going to be very difficult. Is your goal going to be to make slight adaptations to the images? Or to synthesize completely new images? Image data is very high dimensional, and generalising high dimensional data from very few samples is near impossible, no matter what technique you employ.</p>
  
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Answers.CreationDate: 2023-04-17T11:33:02.753Z
Answers.Text: I'm partially agree. It was correct few years ago, that it's very challenging to create synthetic data from 10-15 images. Recently there are works of creating an synthetic images even from single image. For example SinGan paper: https://tamarott.github.io/SinGAN.htm.
Answers.UserDisplayName: Michael D
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Answers.CreationDate: 2023-04-17T11:47:54.670Z
Answers.Text: Did not know of that paper, nice addition!
Answers.UserDisplayName: Robin van Hoorn
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Answers.CreationDate: 2023-04-17T12:16:30.227Z
Answers.Text: It got best paper award at ICCV 2019.
Answers.UserDisplayName: Michael D
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Answers.UserDisplayName: Robin van Hoorn
Body: <p>I'm looking a references (papers / works) for <strong>synthetic image generation</strong> from <strong>small datasets</strong>. By small dataset, I mean 10-50 images.</p>
  <p>I assume, that the best approaches should be based GAN (<strong>cGAN</strong> ?) or Diffusion Model.</p>
  <p>Meanwhile I've found the following:</p>
  <p><a href="https://arxiv.org/abs/2101.04775" rel="nofollow noreferrer">Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis</a></p>
  <p><a href="https://arxiv.org/abs/1904.01774" rel="nofollow noreferrer">Image Generation From Small Datasets via Batch Statistics Adaptation</a></p>
  
Tags: <computer-vision><reference-request><generative-adversarial-networks><image-generation>
Title: References for synthetic images generation from small datasets (~10-50 images)
UserDisplayName: Michael D
ThreadText: # References for synthetic images generation from small datasets (~10-50 images)
  
  (Asked by: username_0 on 2023-04-10)
  
  I'm looking a references (papers / works) for **synthetic image generation** from **small datasets**. By small dataset, I mean 10-50 images.
  
  I assume, that the best approaches should be based GAN (**cGAN** ?) or Diffusion Model.
  
  Meanwhile I've found the following:
  
  [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://arxiv.org/abs/2101.04775)
  
  [Image Generation From Small Datasets via Batch Statistics Adaptation](https://arxiv.org/abs/1904.01774)
  
  **Tags:** computer-vision, reference-request, generative-adversarial-networks, image-generation
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-04-11. Score: 2
  
  The references you stated indeed are the right way to go regarding small dataset image synthesis.
  
  I'd research the space of few-shot image synthesis for what is out there, but something along the line of StyleGAN2 is a logical direction to look into.
  
  In addition, I'd like to state specifically that training a synthesizer on only 10-50 images is always going to be very difficult. Is your goal going to be to make slight adaptations to the images? Or to synthesize completely new images? Image data is very high dimensional, and generalising high dimensional data from very few samples is near impossible, no matter what technique you employ.
  
  > **Comments:**
  > **username_0**: I'm partially agree. It was correct few years ago, that it's very challenging to create synthetic data from 10-15 images. Recently there are works of creating an synthetic images even from single image. For example SinGan paper: https://tamarott.github.io/SinGAN.htm.  
  > **username_1**: Did not know of that paper, nice addition!  
  > **username_0**: It got best paper award at ICCV 2019.  
  
  ---
  
  