When Cats meet GANs
Updated: Apr 21, 2022
Please refer to the Project webpage for result images
Part 1 DCGAN
1.1 Implement Data Augmentation
Deluxe data augmentation helps the model to be more robust.
1.2 DCGAN - Discriminator
The calculation of padding is:
, where m is the output size and n is the input size. p is the padding, K is the kernel size, and S is the stride. Given the size is downsampled by scale 2, we know n = 2m. With K = 4 and S = 2, we will have
which means padding is 1.
1.3 DCGAN - Generator
The design of the first layer in DCGenerator is using conv, instead of up_conv. The idea is to use padding 3, kernel size 4, and stride 1 to obtain a 4x4 output. I also replaced nn.ReLU with nn.LeakyReLU for its better performance.
As we can see, the result of the Deluxe data augmentation + full diffaug configuration with more iterations has better quality and resolution.