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• W3006033959 abstract "We present a GAN-Lmser network for the problem of transforming an image from one domain A to another B. The proposed network is based on CNN-Lmser, a recent further extension to deep convolutional layers from least mean square error reconstruction (Lmser) network, which was originally proposed in 1991. Specifically, in GAN-Lmser, the two directions, A-to-B and B-to-A, share the same architecture and symmetrically the same weights, by following the duality in bidirectional architecture (DBA) and duality connection weights (DCW) of Lmser, and an adversarial loss from GAN(generative adversarial network) was added to Lmser. Compared with the famous image-to-image translation model CycleGAN, the GAN-Lmser is compact with a significantly reduced number of parameters and is able to transfer learning through weight sharing between the two directions. Experiments demonstrate that GAN-Lmser is at least comparable to CycleGAN in benchmark datasets, and is robust when the training sample size is small." @default.
• W3006033959 created "2020-02-24" @default.
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• W3006033959 creator A5073963268 @default.
• W3006033959 date "2019-11-01" @default.
• W3006033959 modified "2023-09-26" @default.
• W3006033959 title "GLmser: A GAN-Lmser Network for Image-to-Image Translation" @default.
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• W3006033959 doi "https://doi.org/10.1109/ictai.2019.00087" @default.
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