Generating Anime Characters Using DCGANs

As we know, convolution layers are really good at processing images. They are capable of learning important features, such as edges, shapes, and complex objects, effectively, as shown in neural networks, such as Inception, AlexNet, Visual Geometry Group (VGG), and ResNet. Ian Goodfellow and others proposed a Generative Adversarial Network (GAN) with dense layers in their paper titled Generative Adversarial Nets, which can be found at the following link: https://arxiv.org/pdf/1406.2661.pdf. Complex neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) were not initially tested in GANs. The development of Deep Convolutional Generative Adversarial Networks (DCGANs) was an important step toward using CNNs for image generation. A DCGAN uses convolutional layers instead...