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• W3007462527 abstract "Image translators have been successfully applied to many important low level image processing tasks. However, classical network architecture of image translator like U-Net, is borrowed from other vision tasks like biomedical image segmentation. This straightforward adaptation may not be optimal and could cause redundancy in the network structure. In this paper, we propose an automatic architecture searching method for image translator. By utilizing evolutionary algorithm, we investigate a more efficient network architecture which costs less computation resources and achieves better performance than the original one. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness of the proposed method. Moreover, we transplant the searched network architecture to other datasets which are not involved in the architecture searching procedure. Efficiency of the searched architecture on these datasets further demonstrates the generalization of the method." @default.
• W3007462527 created "2020-03-06" @default.
• W3007462527 creator A5062581018 @default.
• W3007462527 creator A5066372151 @default.
• W3007462527 date "2020-02-26" @default.
• W3007462527 modified "2023-09-27" @default.
• W3007462527 title "Automatically Searching for U-Net Image Translator Architecture." @default.
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• W3007462527 hasPublicationYear "2020" @default.
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