Attention-Aware Generative Adversarial Networks (ATA-GANs)

Kastaniotis, Dimitris; Ntinou, Ioanna; Tsourounis, Dimitrios; Economou, George; Fotopoulos, S.

doi:10.48550/arxiv.1802.09070

Cited by 1 publication

(2 citation statements)

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“…Chen et al propose AttentionGAN [6], which uses an extra attention network to generate attention maps, so that major attention can be paid to objects of interests. Kastaniotis et al [12] present ATAGAN, which use a teacher network to produce attention maps. Zhang et al [45] propose the Self-Attention Generative Adversarial Networks (SAGAN) for image generation task.…”

Section: Related Workmentioning

confidence: 99%

“…More importantly, we have to make an assumption that the object shape should not change after applying semantic modification. Another option is to train an extra model to detect the object masks and fit them into the generated image patches [6], [12]. In this case, we need to increase the number of parameters of our network, which consequently increases the training complexity both in time and space.…”

Section: Introductionmentioning

confidence: 99%

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Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

Tang

Sebe

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

125

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State-of-the-art models for unpaired image-to-image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss. The intuition behind these models is that if we translate from one domain to the other and back again we should arrive at where we started. However, existing methods always adopt a symmetric network architecture to learn both forward and backward cycles. Because of the task complexity and cycle input difference between the source and target image domains, the inequality in bidirectional forward-backward cycle translations is significant and the amount of information between two domains is different. In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks. Moreover, the training stage of existing methods has the common problem of model collapse that degrades the quality of the generated images, thus we explore different optimization losses for better training of AsymmetricGAN, and thus make image-to-image translation with higher consistency and better stability. Extensive experiments on both supervised and unsupervised generative tasks with several publicly available datasets demonstrate that the proposed AsymmetricGAN achieves superior model capacity and better generation performance compared with existing GAN models. To the best of our knowledge, we are the first to investigate the asymmetric GAN framework on both unsupervised and supervised image-to-image translation tasks. The source code, data and trained models are available at https://github.com/Ha0Tang/AsymmetricGAN.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%