The Truncation Trick is a latent sampling procedure for generative adversarial networks, where we sample $z$ from a truncated normal (where values which fall outside a range are resampled to fall inside that range). The original implementation was in Megapixel Size Image Creation with GAN. In BigGAN, the authors find this provides a boost to the Inception Score and FID.
Source: Megapixel Size Image Creation using Generative Adversarial NetworksPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Generation | 35 | 33.65% |
Conditional Image Generation | 14 | 13.46% |
Super-Resolution | 5 | 4.81% |
Image Super-Resolution | 3 | 2.88% |
Image-to-Image Translation | 3 | 2.88% |
Bias Detection | 2 | 1.92% |
Colorization | 2 | 1.92% |
Image Restoration | 2 | 1.92% |
Model Compression | 2 | 1.92% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |