Comparing Generative Adversarial Network Techniques for Image Creation and Modification
This work addresses image creation and modification for computer vision applications, but it is incremental as it primarily compares existing methods.
The paper compares various GAN techniques for image generation and modification, finding that adding an encoder enables image reconstruction and conditional GANs allow attribute alteration, but using a Capsule Network as the discriminator results in lower-quality generated images compared to standard CNNs.
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder to the network, making it possible to encode images to the latent space of the GAN. The generator, discriminator and encoder are parameterized by deep convolutional neural networks. For the discriminator network we experimented with using the novel Capsule Network, a state-of-the-art technique for detecting global features in images. Experiments are performed using a digit and face dataset, with various visualizations illustrating the results. The results show that using the encoder network it is possible to reconstruct images. With the conditional GAN we can alter visual attributes of generated or encoded images. The experiments with the Capsule Network as discriminator result in generated images of a lower quality, compared to a standard convolutional neural network.