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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3076487
|2 doi
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|a pubmed25n1082.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Tan, Zhentao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Efficient Semantic Image Synthesis via Class-Adaptive Normalization
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| 264 |
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|c 2022
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 08.08.2022
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|a Date Revised 14.09.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis in T. Park et al. 2019 which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away. Despite its impressive performance, a more thorough understanding of the advantages inside the box is still highly demanded to help reduce the significant computation and parameter overhead introduced by this novel structure. In this paper, from a return-on-investment point of view, we conduct an in-depth analysis of the effectiveness of this spatially-adaptive normalization and observe that its modulation parameters benefit more from semantic-awareness rather than spatial-adaptiveness, especially for high-resolution input masks. Inspired by this observation, we propose class-adaptive normalization (CLADE), a lightweight but equally-effective variant that is only adaptive to semantic class. In order to further improve spatial-adaptiveness, we introduce intra-class positional map encoding calculated from semantic layouts to modulate the normalization parameters of CLADE and propose a truly spatially-adaptive variant of CLADE, namely CLADE-ICPE. Through extensive experiments on multiple challenging datasets, we demonstrate that the proposed CLADE can be generalized to different SPADE-based methods while achieving comparable generation quality compared to SPADE, but it is much more efficient with fewer extra parameters and lower computational cost. The code and pretrained models are available at https://github.com/tzt101/CLADE.git
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Chen, Dongdong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chu, Qi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chai, Menglei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liao, Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a He, Mingming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yuan, Lu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hua, Gang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Nenghai
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 9 vom: 29. Sept., Seite 4852-4866
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:9
|g day:29
|g month:09
|g pages:4852-4866
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3076487
|3 Volltext
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