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 cross-image feature

09b6e009612875dd0a7291d5f4fd8b49-Supplemental-Conference.pdf

Neural Information Processing Systems

We use the PyTorch toolkit to implement our inpainting network with CICM. The network is optimized by the Adam solver for 400,000 iterations. The initial learning rate is 0.0001, which is linearly decayed during the network training. In our implementation, we use a warm-up strategy to pre-train the backbone network for 50,000 iterations. The encoder of the pre-trained backbone is used to compute the regional features of different images.

  Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)

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09b6e009612875dd0a7291d5f4fd8b49-Paper-Conference.pdf

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  Genre: Research Report (1.00)
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Cross-Image Context for Single Image Inpainting - Supplementary Material - Tingliang Feng, Wei Feng, Weiqi Li, Di Lin College of Intelligence and Computing, Tianjin University

Neural Information Processing Systems

We use the PyTorch toolkit to implement our inpainting network with CICM. The network is optimized by the Adam solver for 400,000 iterations. The initial learning rate is 0.0001, which is linearly decayed during the network training. We randomly crop and flip the training images to augment the data. In our implementation, we use a warm-up strategy to pre-train the backbone network for 50,000 iterations.

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  Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.48)

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Cross-Image Context for Single Image Inpainting Tingliang Feng, Wei Feng, Weiqi Li, Di Lin College of Intelligence and Computing, Tianjin University

Neural Information Processing Systems

Visual context is of crucial importance for image inpainting.

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  Genre: Research Report (1.00)
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