mit-han-lab/data-efficient-gans
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
This project helps image creators or researchers generate realistic images even when they have very few training examples. You provide a small collection of images (e.g., just 100 pictures of pandas or specific landmarks), and it generates new, high-quality images that look like they belong to that collection. It's ideal for anyone in creative fields, scientific imaging, or research who needs to expand a limited visual dataset.
1,313 stars. No commits in the last 6 months.
Use this if you need to create diverse, high-fidelity images from a very small existing dataset, without needing extensive pre-training.
Not ideal if you already have a very large dataset for image generation or if your primary goal is not image synthesis from limited data.
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Python
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BSD-2-Clause
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Sep 24, 2024
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