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 | 39 | 16.81% |
Conditional Image Generation | 15 | 6.47% |
Reinforcement Learning | 7 | 3.02% |
Multi-agent Reinforcement Learning | 7 | 3.02% |
Super-Resolution | 6 | 2.59% |
Reinforcement Learning (RL) | 6 | 2.59% |
Decision Making | 5 | 2.16% |
Denoising | 4 | 1.72% |
Diversity | 4 | 1.72% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |