According to SA-GAN, one of the problems with the general GAN is that it tries to calculate relations between distant features only as a convolution layer that extracts local features. SA-GAN solved this problem through the Self-Attension layer.
RN is explicitly compute relations between distant features.
(If you want to know more about RN and SA-GAN, read linked papers.)
I thought about using RN to create a discriminator to solve the problem. Experimental results show that this method has a generalization effect and relation calculation effect as compared with DCGAN, but it did not seem to have a perfectly good effect. However, it is expected that there will be a better effect if there is a fine network configuration.
- Python3
- Pytorch 1.0.0
- TensorBoardX
train
python train.py
--batch_size [64]
--image_size [256]
--n_epoch [200]
--lr_d [2e-4]
--lr_g [2e-4]
--dataset [./celebA-HQ]
--init [kaiming]
--nz [128]
--ngf [64]
--ndf [64]
--result_dir [result]
--model_dir [models]
--gan_type vanilla|lsgan (Now LSGAN does not work properly.)
--g_type [dcgan]
--d_type [rngan]
--resume []
--seed [12345]
--n_cpu [4]
Train with celebA-HQ dataset resolution as 256x256
RNGAN Result (RN discriminator and DCGAN generator)
DCGAN Result (DCGAN discriminator and DCGAN generator)
