Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Deep Convolutional GAN (DCGAN) <s> Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets. <s> BIB001 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Deep Convolutional GAN (DCGAN) <s> In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations. <s> BIB002
DCGAN is the first work that applied a deconvolutional neural networks architecture for G BIB002 . Figure. 4 illustrates the proposed architecture for G. Deconvolution is proposed to visualize the features for a CNN and has shown good performance for CNNs visualization BIB001 . DCGAN deploys the spatial up-sampling ability of the deconvolution operation for G, which enables the generation of higher resolution images using GANs.