Interpretation of Latent Codes in InfoGAN with SAR Images

Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the ta...
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Autor*in:	Zhenpeng Feng [verfasserIn] Miloš Daković [verfasserIn] Hongbing Ji [verfasserIn] Xianda Zhou [verfasserIn] Mingzhe Zhu [verfasserIn] Xiyang Cui [verfasserIn] Ljubiša Stanković [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	interpreting neural networks InfoGAN SAR image synthesis

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 5, p 1254
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:5, p 1254

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15051254

Katalog-ID:	DOAJ087975564

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allfields	10.3390/rs15051254 doi (DE-627)DOAJ087975564 (DE-599)DOAJ7dadd6ba920f4348b7a097ad48273a54 DE-627 ger DE-627 rakwb eng Zhenpeng Feng verfasserin aut Interpretation of Latent Codes in InfoGAN with SAR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect. interpreting neural networks InfoGAN SAR image synthesis Science Q Miloš Daković verfasserin aut Hongbing Ji verfasserin aut Xianda Zhou verfasserin aut Mingzhe Zhu verfasserin aut Xiyang Cui verfasserin aut Ljubiša Stanković verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 5, p 1254 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:5, p 1254 https://doi.org/10.3390/rs15051254 kostenfrei https://doaj.org/article/7dadd6ba920f4348b7a097ad48273a54 kostenfrei https://www.mdpi.com/2072-4292/15/5/1254 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 5, p 1254
spelling	10.3390/rs15051254 doi (DE-627)DOAJ087975564 (DE-599)DOAJ7dadd6ba920f4348b7a097ad48273a54 DE-627 ger DE-627 rakwb eng Zhenpeng Feng verfasserin aut Interpretation of Latent Codes in InfoGAN with SAR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect. interpreting neural networks InfoGAN SAR image synthesis Science Q Miloš Daković verfasserin aut Hongbing Ji verfasserin aut Xianda Zhou verfasserin aut Mingzhe Zhu verfasserin aut Xiyang Cui verfasserin aut Ljubiša Stanković verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 5, p 1254 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:5, p 1254 https://doi.org/10.3390/rs15051254 kostenfrei https://doaj.org/article/7dadd6ba920f4348b7a097ad48273a54 kostenfrei https://www.mdpi.com/2072-4292/15/5/1254 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 5, p 1254
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allfieldsSound	10.3390/rs15051254 doi (DE-627)DOAJ087975564 (DE-599)DOAJ7dadd6ba920f4348b7a097ad48273a54 DE-627 ger DE-627 rakwb eng Zhenpeng Feng verfasserin aut Interpretation of Latent Codes in InfoGAN with SAR Images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect. interpreting neural networks InfoGAN SAR image synthesis Science Q Miloš Daković verfasserin aut Hongbing Ji verfasserin aut Xianda Zhou verfasserin aut Mingzhe Zhu verfasserin aut Xiyang Cui verfasserin aut Ljubiša Stanković verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 5, p 1254 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:5, p 1254 https://doi.org/10.3390/rs15051254 kostenfrei https://doaj.org/article/7dadd6ba920f4348b7a097ad48273a54 kostenfrei https://www.mdpi.com/2072-4292/15/5/1254 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 5, p 1254
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authorswithroles_txt_mv	Zhenpeng Feng @@aut@@ Miloš Daković @@aut@@ Hongbing Ji @@aut@@ Xianda Zhou @@aut@@ Mingzhe Zhu @@aut@@ Xiyang Cui @@aut@@ Ljubiša Stanković @@aut@@
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author	Zhenpeng Feng
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topic_title	Interpretation of Latent Codes in InfoGAN with SAR Images interpreting neural networks InfoGAN SAR image synthesis
topic	misc interpreting neural networks misc InfoGAN misc SAR image synthesis misc Science misc Q
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title	Interpretation of Latent Codes in InfoGAN with SAR Images
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title_full	Interpretation of Latent Codes in InfoGAN with SAR Images
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title_sort	interpretation of latent codes in infogan with sar images
title_auth	Interpretation of Latent Codes in InfoGAN with SAR Images
abstract	Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect.
abstractGer	Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect.
abstract_unstemmed	Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect.
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title_short	Interpretation of Latent Codes in InfoGAN with SAR Images
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