Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently s...
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2017
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