In the previous section, we learned about generating images using GANs. However, we have already seen that Convolutional Neural Networks (CNNs) perform better in the context of images when compared to vanilla neural networks. In this section, we will learn about generating images using Deep Convolutional Generative Adversarial Networks (DCGANs), which use convolution and pooling operations in the model.
First, let's understand the technique we will leverage to generate an image using a set of 100 random numbers. We will first convert noise into a shape of batch size x 100 x 1 x 1. The reason for appending additional channel information in DCGANs and not doing it in the GAN section is that we will leverage CNNs in this section, which requires inputs in the form of batch size x channels x height x width.
Next, we convert the generated noise into an image by leveraging
ConvTranspose2d.
As we learned in Chapter 9, Image Segmentation, ConvTranspose2d...
