2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019
DOI: 10.1109/cvprw.2019.00272
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Robust Image Colorization Using Self Attention Based Progressive Generative Adversarial Network

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Cited by 11 publications
(9 citation statements)
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References 26 publications
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“…In the last decade, several methods [24][25][26][27][28][29][30][31] have been introduced for image colorization tasks. Deshpande et al [26] using a variational autoencoder (VAE) to learn a low dimensional embedding of color fields.…”
Section: Image Colorizationmentioning
confidence: 99%
See 1 more Smart Citation
“…In the last decade, several methods [24][25][26][27][28][29][30][31] have been introduced for image colorization tasks. Deshpande et al [26] using a variational autoencoder (VAE) to learn a low dimensional embedding of color fields.…”
Section: Image Colorizationmentioning
confidence: 99%
“…Nazeri et al [30] use a conditional Deep Convolutional Generative Adversarial Network (DCGAN) to generalize the process of automatic image colorization. Sharma et al [31] proposed Self-Attention-based Progressive Generative Adversarial Network (RIC-SPGAN) to perform the denoising and colorization of the image. However, these methods mainly focus on colorization of gray-scale images, which are mainly applicable to the restoration of aged or degraded images.…”
Section: Image Colorizationmentioning
confidence: 99%
“…A lot of work has proposed fully automatic colorization [3,10,15,19,28,32,34,42]. Most of it was based on the deep neural network (DNN) [40,41] and achieved high-quality image colorization.…”
Section: Introductionmentioning
confidence: 99%
“…With the development of generative adversarial networks (GANs) [21], Chia et al [5] utilized a GAN to propose style conversion networks that can transfer an image style from another image, which also can be applied to transferring color to a grayscale image, or to synthesize a color image from a grayscale image [10,15,28,32]. The CycleGAN [44], in particular, enables the learning of unpaired datasets by applying cycle-consistency.…”
Section: Introductionmentioning
confidence: 99%
“…为了克服 GANs 不稳定性问题, 研究者们通过优化网络结构 [2,4,15,16] 、 改变梯度更新方法 [13,17,18] 、 修改目标函数 [3, 12, 19∼21] , 以及对判别器实施正则化 [22∼24] 或惩罚 [25,26] 等技术来提高 GANs 训练的 稳定性. 其中, 惩罚技术是效果较好的一类方法.…”
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