Face editing with GAN -- A Review

In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator. The generator is used to produce new samples or pieces of content, while the discriminator is used to recognize whether the piece of content is real or generated. What makes it different from other generative models is its ability to learn unlabeled samples. In this review paper, we will discuss the evolution of GANs, several improvements proposed by the authors and a brief comparison between the different models. Index Terms generative adversarial networks, unsupervised learning, deep learning

Towards Co-Creative Generative Adversarial Networks for Fashion Designers

Originating from the premise that Generative Adversarial Networks (GANs) enrich creative processes rather than diluting them, we describe an ongoing PhD project that proposes to study GANs in a co-creative context. By asking How can GANs be applied in co-creation, and in doing so, how can they contribute to fashion design processes? the project sets out to investigate co-creative GAN applications and further develop them for the specific application area of fashion design. We do so by drawing on the field of mixed-initiative co-creation. Combined with the technical insight into GANs' functioning, we aim to understand how their algorithmic properties translate into interactive interfaces for co-creation and propose new interactions.Comment: Published at GenAICHI, CHI 2022 Worksho

Direct Adversarial Training: A New Approach for Stabilizing The Training Process of GANs

Generative Adversarial Networks (GANs) are the most popular models for image generation by optimizing discriminator and generator jointly and gradually. However, instability in training process is still one of the open problems for all GAN-based algorithms. In order to stabilize training, some regularization and normalization techniques have been proposed to make discriminator meet the Lipschitz continuity constraint. In this paper, a new approach inspired by works on adversarial attack is proposed to stabilize the training process of GANs. It is found that sometimes the images generated by the generator play a role just like adversarial examples for discriminator during the training process, which might be a part of the reason of the unstable training. With this discovery, we propose to introduce a adversarial training method into the training process of GANs to improve its stabilization. We prove that this DAT can limit the Lipschitz constant of the discriminator adaptively. The advanced performance of the proposed method is verified on multiple baseline and SOTA networks, such as DCGAN, WGAN, Spectral Normalization GAN, Self-supervised GAN and Information Maximum GAN