Abstract
The processing of facial images is an important task, because it is required for a large number of real-world applications. As deep-learning models evolve, they require a huge number of images for training. In reality, however, the number of images available is limited. Generative adversarial networks (GANs) have thus been utilized for database augmentation, but they suffer from unstable training, low visual quality, and a lack of diversity. In this paper, we propose an auto-encoder-based GAN with an enhanced network structure and training scheme for Database (DB) augmentation and image synthesis. Our generator and decoder are divided into two separate modules that each take input vectors for low-level and high-level features; these input vectors affect all layers within the generator and decoder. The effectiveness of the proposed method is demonstrated by comparing it with baseline methods. In addition, we introduce a new scheme that can combine two existing images without the need for extra networks based on the auto-encoder structure of the discriminator in our model. We add a novel double-constraint loss to make the encoded latent vectors equal to the input vectors.
Highlights
In the last few years, deep neural networks (DNNs) have been successfully applied to a range of computer vision tasks, including classification [1,2,3], detection [4,5,6], segmentation [7,8], and information fusion [9,10]
Motivated by StyleGAN, we propose a generator that takes two latent vectors as input based on the scale-specific role of each layer in the generator
BEGAN has a discriminator with an auto-encoder structure, meaning that the output of the discriminator is an image of the same size as the input
Summary
In the last few years, deep neural networks (DNNs) have been successfully applied to a range of computer vision tasks, including classification [1,2,3], detection [4,5,6], segmentation [7,8], and information fusion [9,10]. Because data augmentation is essential for the effective training of DNNs, and because there are numerous image-to-image translation and information fusion problems that need to be overcome, deep generative models have received significant attention. In this field, research on facial datasets has been active, because they have a large number of real-world applications, such as facial classification and the opening of closed eyes in photos. Research on facial datasets has been active, because they have a large number of real-world applications, such as facial classification and the opening of closed eyes in photos Despite this increase in research interest, implementing generative models remains challenging because the process required to generate realistic images from low-level to high-level information is complex. The key principle underlying the use of a GAN is to ensure that the probability distribution of the generated data is close to that of the real data via the adversarial training of the generator and discriminator
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