nupurkmr9/vision-aided-gan
Ensembling Off-the-shelf Models for GAN Training (CVPR 2022 Oral)
This project helps researchers and developers working with Generative Adversarial Networks (GANs) to create more realistic and high-quality synthetic images. It takes existing datasets and leverages the collective 'knowledge' of a bank of pre-trained computer vision models to guide the GAN's training, producing outputs that closely resemble real-world images. This is ideal for those who need to generate highly convincing visual content, especially when real data is scarce.
421 stars. No commits in the last 6 months.
Use this if you are developing image generation models and want to significantly improve the realism and quality of your synthetic images, even with limited training data.
Not ideal if your primary goal is not image generation, or if you need to train models from scratch without leveraging existing vision model knowledge.
Stars
421
Forks
32
Language
Python
License
MIT
Category
Last pushed
Sep 09, 2022
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