Lessons Learned from the Training of GANs on Artificial Datasets

13 Jul 2020Shichang Tang

Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. However, they are often trained on image datasets with either too few samples or too many classes belonging to different data distributions... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Conditional Image Generation CIFAR-10 MIX-MHingeGAN Inception score 10.21 # 1
FID 3.6 # 1
Image Generation CIFAR-10 MIX-BigGAN Inception score 9.67 # 1
FID 8.17 # 3

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet