mcVAE: disentangling by mean constraint

Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classifica...
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Autor*in:	Hu, Ming-fei [verfasserIn] Liu, Ze-yu Liu, Jian-wei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Variational autoencoder Disentanglement Representation learning

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 40(2023), 2 vom: 06. Apr., Seite 1229-1243
Übergeordnetes Werk:	volume:40 ; year:2023 ; number:2 ; day:06 ; month:04 ; pages:1229-1243

Links:	Volltext

DOI / URN:	10.1007/s00371-023-02843-9

Katalog-ID:	SPR054485428

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520			\|a Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets.
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allfields	10.1007/s00371-023-02843-9 doi (DE-627)SPR054485428 (SPR)s00371-023-02843-9-e DE-627 ger DE-627 rakwb eng Hu, Ming-fei verfasserin aut mcVAE: disentangling by mean constraint 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213 Liu, Ze-yu aut Liu, Jian-wei aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 2 vom: 06. Apr., Seite 1229-1243 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243 https://dx.doi.org/10.1007/s00371-023-02843-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2023 2 06 04 1229-1243
spelling	10.1007/s00371-023-02843-9 doi (DE-627)SPR054485428 (SPR)s00371-023-02843-9-e DE-627 ger DE-627 rakwb eng Hu, Ming-fei verfasserin aut mcVAE: disentangling by mean constraint 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213 Liu, Ze-yu aut Liu, Jian-wei aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 2 vom: 06. Apr., Seite 1229-1243 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243 https://dx.doi.org/10.1007/s00371-023-02843-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2023 2 06 04 1229-1243
allfields_unstemmed	10.1007/s00371-023-02843-9 doi (DE-627)SPR054485428 (SPR)s00371-023-02843-9-e DE-627 ger DE-627 rakwb eng Hu, Ming-fei verfasserin aut mcVAE: disentangling by mean constraint 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213 Liu, Ze-yu aut Liu, Jian-wei aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 2 vom: 06. Apr., Seite 1229-1243 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243 https://dx.doi.org/10.1007/s00371-023-02843-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2023 2 06 04 1229-1243
allfieldsGer	10.1007/s00371-023-02843-9 doi (DE-627)SPR054485428 (SPR)s00371-023-02843-9-e DE-627 ger DE-627 rakwb eng Hu, Ming-fei verfasserin aut mcVAE: disentangling by mean constraint 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213 Liu, Ze-yu aut Liu, Jian-wei aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 2 vom: 06. Apr., Seite 1229-1243 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243 https://dx.doi.org/10.1007/s00371-023-02843-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2023 2 06 04 1229-1243
allfieldsSound	10.1007/s00371-023-02843-9 doi (DE-627)SPR054485428 (SPR)s00371-023-02843-9-e DE-627 ger DE-627 rakwb eng Hu, Ming-fei verfasserin aut mcVAE: disentangling by mean constraint 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213 Liu, Ze-yu aut Liu, Jian-wei aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 2 vom: 06. Apr., Seite 1229-1243 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243 https://dx.doi.org/10.1007/s00371-023-02843-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 40 2023 2 06 04 1229-1243
language	English
source	Enthalten in The visual computer 40(2023), 2 vom: 06. Apr., Seite 1229-1243 volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243
sourceStr	Enthalten in The visual computer 40(2023), 2 vom: 06. Apr., Seite 1229-1243 volume:40 year:2023 number:2 day:06 month:04 pages:1229-1243
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Variational autoencoder Disentanglement Representation learning
isfreeaccess_bool	false
container_title	The visual computer
authorswithroles_txt_mv	Hu, Ming-fei @@aut@@ Liu, Ze-yu @@aut@@ Liu, Jian-wei @@aut@@
publishDateDaySort_date	2023-04-06T00:00:00Z
hierarchy_top_id	254910734
id	SPR054485428
language_de	englisch
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author	Hu, Ming-fei
spellingShingle	Hu, Ming-fei misc Variational autoencoder misc Disentanglement misc Representation learning mcVAE: disentangling by mean constraint
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topic_title	mcVAE: disentangling by mean constraint Variational autoencoder (dpeaa)DE-He213 Disentanglement (dpeaa)DE-He213 Representation learning (dpeaa)DE-He213
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title	mcVAE: disentangling by mean constraint
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title_full	mcVAE: disentangling by mean constraint
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author-letter	Hu, Ming-fei
doi_str_mv	10.1007/s00371-023-02843-9
title_sort	mcvae: disentangling by mean constraint
title_auth	mcVAE: disentangling by mean constraint
abstract	Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Disentanglement tends to automatically learn and separate the interpretable factors of variation hidden in the data. Disentangled representations are more transferable and robust for the chosen model, and they are commonly used in image attack detection and anti-fraud, as well as classification and recommendation systems in special situations. As a popular method for learning unsupervised disentanglement, β-VAE re-weights the KL divergence by an adjustable hyperparameter. However, good disentangled representations always lead to blurry reconstructions and mode collapse on complex datasets. We find that the variance vector of the variational posterior is related to the nature of the dataset and representation space, limiting its value to 1 is not reasonable enough. More importantly, constraining mean variable alone can achieve better disentanglement and reconstruction performance. Therefore, we introduce mean constraint VAE, a simple and effective replacement of the β-VAE for improving the poor reconstruction and learning a higher degree of disentanglement. In addition, a classifier-free measure of disentanglement called variance proportion metric is proposed. Experiments show that our framework outperforms β-VAE on several benchmark datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	mcVAE: disentangling by mean constraint
url	https://dx.doi.org/10.1007/s00371-023-02843-9
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score	7.3999233

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mcVAE: disentangling by mean constraint

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