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We find it is\nfundamentally difficult to achieve comprehensive style modeling using\n1-dimensional style embedding. Motivated by this, we introduce CoMatch Layer\nthat learns to match the second order feature statistics with the target\nstyles. With the CoMatch Layer, we build a Multi-style Generative Network\n(MSG-Net), which achieves real-time performance. We also employ an specific\nstrategy of upsampled convolution which avoids checkerboard artifacts caused by\nfractionally-strided convolution. Our method has achieved superior image\nquality comparing to state-of-the-art approaches. The proposed MSG-Net as a\ngeneral approach for real-time style transfer is compatible with most existing\ntechniques including content-style interpolation, color-preserving, spatial\ncontrol and brush stroke size control. MSG-Net is the first to achieve\nreal-time brush-size control in a purely feed-forward manner for style\ntransfer. Our implementations and pre-trained models for Torch, PyTorch and\nMXNet frameworks will be publicly available.","url_abs":"http://arxiv.org/abs/1703.06953v2","url_pdf":"http://arxiv.org/pdf/1703.06953v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/germanjke/StyleTransformerGANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/habout632/gans","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/lxy5513/Multi-Style-Transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/noufali/VideoML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multi-style-generative-network-for-real-time","repo_url":"https://github.com/sonnguyentruong129/msgnet-tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.06953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06953"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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