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- W2214802144 abstract "We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/ vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin." @default.
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- W2214802144 date "2016-06-01" @default.
- W2214802144 modified "2023-10-17" @default.
- W2214802144 title "Deeply-Recursive Convolutional Network for Image Super-Resolution" @default.
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- W2214802144 doi "https://doi.org/10.1109/cvpr.2016.181" @default.
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