There is my summer 2021 project about different GAN approaches in generating abstract paintings.
Архитектура дискриминатора и генератора, которую я использовал в каждом варианте гана (в LSGAN и WCGAN у дискриминатора отсутствовала сигмоидная функция активации на выходе)
Discriminator:
Sequential(
(0): Conv2d(3, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): LeakyReLU(negative_slope=0.2, inplace=True)
(2): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(3): LeakyReLU(negative_slope=0.2, inplace=True)
(4): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(5): LeakyReLU(negative_slope=0.2, inplace=True)
(6): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
(7): Flatten(start_dim=1, end_dim=-1)
(8): Sigmoid()
)Generator:
Sequential(
(0): ConvTranspose2d(128, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
(3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): LeakyReLU(negative_slope=0.2)
(6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): LeakyReLU(negative_slope=0.2)
(9): ConvTranspose2d(128, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(10): Tanh()
)DCGAN Loss: Discriminator:
binary_cross_entropy(D(G(z)), x)Generator:
binary_cross_entropy(D(G(z)), x)LSGAN Loss: Discriminator:
0.5 * (torch.mean((D(x) - 1) ** 2) + torch.mean(D(G(z)) ** 2))Generator:
0.5 * torch.mean((D(G(z)) - 1)**2)WCGAN Loss: Discriminator:
-(torch.mean(D(x)) - torch.mean(D(G(z))))Generator:
-torch.mean(D(G(z)))
