Latent Variable Sampling
Truncation Trick
Introduced by Marchesi in Megapixel Size Image Creation using Generative Adversarial NetworksThe 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 Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 38 | 19.39% |
| Conditional Image Generation | 15 | 7.65% |
| Multi-agent Reinforcement Learning | 7 | 3.57% |
| Super-Resolution | 6 | 3.06% |
| Reinforcement Learning (RL) | 6 | 3.06% |
| Decision Making | 5 | 2.55% |
| Denoising | 4 | 2.04% |
| Clustering | 4 | 2.04% |
| Benchmarking | 3 | 1.53% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
