hichoe95/Artifact-Detection-and-Sequential-Ablation
[IJCAI-2022] Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
This tool helps researchers and practitioners working with deep generative models like GANs to identify and fix "artifacts" – unrealistic or defective elements – in the images these models create. It takes a pre-trained generative model and dataset, then outputs information about which internal components (neurons) are causing these artifacts and can generate corrected, higher-quality images. It's for anyone who trains or uses generative AI models and needs to ensure their outputs are photorealistic.
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Use this if you are generating images with GANs and notice recurring imperfections or unrealistic details you want to automatically detect and correct.
Not ideal if you are working with generative models other than PGGAN or StyleGAN2, or if your primary goal is not image generation.
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Nov 19, 2024
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