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  • Writer's pictureLena Du

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.

1.4 Result

As we can see, the result of the Deluxe data augmentation + full diffaug configuration with more iterations has better quality and resolution.

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