Passenger flow forecast is the prerequisite and foundation for urban rail transit planning and operation. With the continuous expansion of rail network scale and the surge of passenger flow, the passenger flow prediction task becomes increasingly important and arduous. This paper presents an overview of the current research on passenger flow forecast in the field of urban rail transit, which mainly incorporates short-term passenger flow forecast, passenger flow forecast under emergency, typical days’ passenger flow forecast and long-term passenger flow forecast for new opening line and extended line. The prediction characteristics in each subfield are discussed and the state-of-the-art forecasting approaches are reviewed. A multitude of existing studies shows that the forecast under different scenarios went through an imbalanced development. There are special prediction procedures and various applicable models in each scenario. Finally, we propose some future research prospects and discuss the potential applications of passenger flow forecast.
{"title":"Passenger flow forecasting approaches for urban rail transit: a survey","authors":"Qiuchi Xue, Wei Zhang, Meiling Ding, Xin Yang, Jianjun Wu, Z. Gao","doi":"10.1080/03081079.2023.2231133","DOIUrl":"https://doi.org/10.1080/03081079.2023.2231133","url":null,"abstract":"Passenger flow forecast is the prerequisite and foundation for urban rail transit planning and operation. With the continuous expansion of rail network scale and the surge of passenger flow, the passenger flow prediction task becomes increasingly important and arduous. This paper presents an overview of the current research on passenger flow forecast in the field of urban rail transit, which mainly incorporates short-term passenger flow forecast, passenger flow forecast under emergency, typical days’ passenger flow forecast and long-term passenger flow forecast for new opening line and extended line. The prediction characteristics in each subfield are discussed and the state-of-the-art forecasting approaches are reviewed. A multitude of existing studies shows that the forecast under different scenarios went through an imbalanced development. There are special prediction procedures and various applicable models in each scenario. Finally, we propose some future research prospects and discuss the potential applications of passenger flow forecast.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"919 - 947"},"PeriodicalIF":2.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44785195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-25DOI: 10.1080/03081079.2023.2223755
Qing‐Hao Zhang, Jun‐Guo Lu
ABSTRACT This paper investigates the admissibility and robust stabilization of fractional-order singular discrete systems with interval uncertainties. Firstly, based on the analysis of the regularity, causality and stability, novel admissibility conditions for nominal fractional-order singular discrete systems are derived including a necessary and sufficient condition in terms of spectral radius and a sufficient condition in terms of non-strict linear matrix inequalities. In order to eliminate the coupling terms and propose strict linear matrix inequality results, another novel admissibility condition is obtained, which is more tractable and reliable with the available linear matrix inequality software solver and more suitable for the controller design compared with the existing results. Secondly, the state feedback controller synthesis for the fractional-order singular discrete systems with interval uncertainties is addressed and the state feedback controller is designed. Finally, the efficiency of the proposed method is demonstrated by two numerical simulation examples.
{"title":"Admissibility and robust stabilization of fractional-order singular discrete systems with interval uncertainties","authors":"Qing‐Hao Zhang, Jun‐Guo Lu","doi":"10.1080/03081079.2023.2223755","DOIUrl":"https://doi.org/10.1080/03081079.2023.2223755","url":null,"abstract":"ABSTRACT This paper investigates the admissibility and robust stabilization of fractional-order singular discrete systems with interval uncertainties. Firstly, based on the analysis of the regularity, causality and stability, novel admissibility conditions for nominal fractional-order singular discrete systems are derived including a necessary and sufficient condition in terms of spectral radius and a sufficient condition in terms of non-strict linear matrix inequalities. In order to eliminate the coupling terms and propose strict linear matrix inequality results, another novel admissibility condition is obtained, which is more tractable and reliable with the available linear matrix inequality software solver and more suitable for the controller design compared with the existing results. Secondly, the state feedback controller synthesis for the fractional-order singular discrete systems with interval uncertainties is addressed and the state feedback controller is designed. Finally, the efficiency of the proposed method is demonstrated by two numerical simulation examples.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"895 - 918"},"PeriodicalIF":2.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42796493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-14DOI: 10.1080/03081079.2023.2222888
Tianyu Hao, G. Song, H. Du
A new stock forecasting model that combines time attention and adaptive particle swarm optimization with LSTM (APSO-TA-LSTM) is proposed to improve the forecasting ability of neural networks for financial time series. The model uses a two-layer LSTM network to encode stock information within the time window and employs time attention to strategically focus on dependencies among time series features for more accurate feature representations. Additionally, the proposed adaptive particle swarm optimization algorithm is used to pick out the key parameters of the network structure and enhance the overall prediction performance. Finally, the experimental results on three stock datasets validate the innovation and effectiveness of our method, and this work will have a broad application prospect in the study of financial time series.
{"title":"APSO-TA-LSTM: a long and short term memory model combining time attention and adaptive particle swarm optimization for stock forecasting","authors":"Tianyu Hao, G. Song, H. Du","doi":"10.1080/03081079.2023.2222888","DOIUrl":"https://doi.org/10.1080/03081079.2023.2222888","url":null,"abstract":"A new stock forecasting model that combines time attention and adaptive particle swarm optimization with LSTM (APSO-TA-LSTM) is proposed to improve the forecasting ability of neural networks for financial time series. The model uses a two-layer LSTM network to encode stock information within the time window and employs time attention to strategically focus on dependencies among time series features for more accurate feature representations. Additionally, the proposed adaptive particle swarm optimization algorithm is used to pick out the key parameters of the network structure and enhance the overall prediction performance. Finally, the experimental results on three stock datasets validate the innovation and effectiveness of our method, and this work will have a broad application prospect in the study of financial time series.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"876 - 893"},"PeriodicalIF":2.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41428433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.1080/03081079.2023.2218017
B. Ucer, Selim Orhun Susam
In this paper, we consider the stress-strength reliability where the strength Y of a component lies between the dependent stress variables and . We propose a copula-based approach for stress-strength reliability having bivariate stress. We obtain R for Farlie-Gumbel-Morgenstern copula with Burr III marginals. Also, we propose a Bernstein copula approximation for evaluating R under the stress-strength setup. We present empirical and maximum likelihood-based estimation procedures and compare their performances by Monte Carlo simulation. We apply the proposed approach to chemical and overt diabetes data for illustration purpose.
{"title":"On the copula-based reliability of stress-strength model under bivariate stress","authors":"B. Ucer, Selim Orhun Susam","doi":"10.1080/03081079.2023.2218017","DOIUrl":"https://doi.org/10.1080/03081079.2023.2218017","url":null,"abstract":"In this paper, we consider the stress-strength reliability where the strength Y of a component lies between the dependent stress variables and . We propose a copula-based approach for stress-strength reliability having bivariate stress. We obtain R for Farlie-Gumbel-Morgenstern copula with Burr III marginals. Also, we propose a Bernstein copula approximation for evaluating R under the stress-strength setup. We present empirical and maximum likelihood-based estimation procedures and compare their performances by Monte Carlo simulation. We apply the proposed approach to chemical and overt diabetes data for illustration purpose.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"842 - 863"},"PeriodicalIF":2.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44769490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-28DOI: 10.1080/03081079.2023.2212849
Qiu Jin, Lingqiang Li
Axiomatization is a lively research direction in fuzzy rough set theory. Fuzzy variable precision rough set (FVPRS) incorporates fault-tolerant factors to fuzzy rough set, so its axiomatic description becomes more complicated and difficult to realize. In this paper, we present an axiomatic approach to FVPRSs based on residuated lattice (L-fuzzy variable precision rough set (LFVPRS)). First, a pair of mappings with three axioms is utilized to characterize the upper (resp., lower) approximation operator of LFVPRS. This is distinct from the characterization on upper (resp., lower) approximation operator of fuzzy rough set, which consists of one mapping with two axioms. Second, utilizing the notion of correlation degree (resp., subset degree) of fuzzy sets, three characteristic axioms are grouped into a single axiom. At last, various special LFVPRS generated by reflexive, symmetric and transitive L-fuzzy relation and their composition are also characterized by axiomatic set and single axiom, respectively.
{"title":"The axiomatic characterization on fuzzy variable precision rough sets based on residuated lattice","authors":"Qiu Jin, Lingqiang Li","doi":"10.1080/03081079.2023.2212849","DOIUrl":"https://doi.org/10.1080/03081079.2023.2212849","url":null,"abstract":"Axiomatization is a lively research direction in fuzzy rough set theory. Fuzzy variable precision rough set (FVPRS) incorporates fault-tolerant factors to fuzzy rough set, so its axiomatic description becomes more complicated and difficult to realize. In this paper, we present an axiomatic approach to FVPRSs based on residuated lattice (L-fuzzy variable precision rough set (LFVPRS)). First, a pair of mappings with three axioms is utilized to characterize the upper (resp., lower) approximation operator of LFVPRS. This is distinct from the characterization on upper (resp., lower) approximation operator of fuzzy rough set, which consists of one mapping with two axioms. Second, utilizing the notion of correlation degree (resp., subset degree) of fuzzy sets, three characteristic axioms are grouped into a single axiom. At last, various special LFVPRS generated by reflexive, symmetric and transitive L-fuzzy relation and their composition are also characterized by axiomatic set and single axiom, respectively.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"820 - 841"},"PeriodicalIF":2.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47138122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-23DOI: 10.1080/03081079.2023.2206130
L. B. D. E. Rosa, M. S. Oliveira, Renan Lima Pereira
ABSTRACT This paper proposes a comprehensive mixed-sensitivity control design for an experimental magnetic levitation (Maglev) system. The control strategy can be seen as an extension of the loop-shaping procedure for discrete-time linear parameter-varying (LPV) systems using linear-fractional representation (LFR). By making use of an efficient quadratic approach given in the form of linear matrix inequalities (LMIs), a functional and computationally attractive gain-scheduling technique is achieved. Despite the rigorous mathematical considerations to obtain the controller, the guidelines to its practical implementation are presented as a straightforward method using LMIs. A detailed modeling of the Maglev plant manufactured by Quanser is carried out to illustrate the procedure, including a description of the nonlinear equations embedding process to obtain a discretized quasi-LPV (qLPV) model. Experimental results demonstrate the effectiveness of the proposed control design.
{"title":"qLPV modeling and mixed-sensitivity L 2 control for a magnetic levitation system","authors":"L. B. D. E. Rosa, M. S. Oliveira, Renan Lima Pereira","doi":"10.1080/03081079.2023.2206130","DOIUrl":"https://doi.org/10.1080/03081079.2023.2206130","url":null,"abstract":"ABSTRACT This paper proposes a comprehensive mixed-sensitivity control design for an experimental magnetic levitation (Maglev) system. The control strategy can be seen as an extension of the loop-shaping procedure for discrete-time linear parameter-varying (LPV) systems using linear-fractional representation (LFR). By making use of an efficient quadratic approach given in the form of linear matrix inequalities (LMIs), a functional and computationally attractive gain-scheduling technique is achieved. Despite the rigorous mathematical considerations to obtain the controller, the guidelines to its practical implementation are presented as a straightforward method using LMIs. A detailed modeling of the Maglev plant manufactured by Quanser is carried out to illustrate the procedure, including a description of the nonlinear equations embedding process to obtain a discretized quasi-LPV (qLPV) model. Experimental results demonstrate the effectiveness of the proposed control design.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"722 - 744"},"PeriodicalIF":2.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47867301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-14DOI: 10.1080/03081079.2023.2208729
Haoxuan Li, Xiangfeng Yang, Yaodong Ni
ABSTRACT Given the existing observations, regression is necessary to predict the relationship between the response variable and the explanatory variable. In general, we assume that the observed data are precise, but in actual life, precise observations are often difficult to be obtained, and most of them are imprecise interval data. As a result, the traditional regression analysis may lead to inaccurate results. When dealing with imprecise observations for more precise regression analysis, uncertainty theory is more appropriate. This paper will introduce the uncertain yield-density regression model and derive the optimal parameters by the least squares method. Besides, we provide residual analysis to obtain the distribution of the model's disturbance term and validate the appropriateness of the disturbance term using uncertain hypothesis testing. The predicted value and confidence interval for the model are also given. Moreover, three numerical examples of uncertain yield-density regression models will be given. Finally, this model will be successfully used in parsnips as an application.
{"title":"Uncertain yield-density regression model with application to parsnips","authors":"Haoxuan Li, Xiangfeng Yang, Yaodong Ni","doi":"10.1080/03081079.2023.2208729","DOIUrl":"https://doi.org/10.1080/03081079.2023.2208729","url":null,"abstract":"ABSTRACT Given the existing observations, regression is necessary to predict the relationship between the response variable and the explanatory variable. In general, we assume that the observed data are precise, but in actual life, precise observations are often difficult to be obtained, and most of them are imprecise interval data. As a result, the traditional regression analysis may lead to inaccurate results. When dealing with imprecise observations for more precise regression analysis, uncertainty theory is more appropriate. This paper will introduce the uncertain yield-density regression model and derive the optimal parameters by the least squares method. Besides, we provide residual analysis to obtain the distribution of the model's disturbance term and validate the appropriateness of the disturbance term using uncertain hypothesis testing. The predicted value and confidence interval for the model are also given. Moreover, three numerical examples of uncertain yield-density regression models will be given. Finally, this model will be successfully used in parsnips as an application.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"777 - 801"},"PeriodicalIF":2.0,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41839694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-09DOI: 10.1080/03081079.2023.2203922
Jinlei Zhang, Hua Li, Shuxin Zhang, Lixing Yang, G. Jin, J. Qi
ABSTRACT Most short-term passenger flow prediction methods only consider absolute errors as the optimization objective, which fails to account for spatial and temporal constraints on the predictions. To overcome these limitations, we propose a deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) to accurately predict network-wide short-term passenger flows of the urban rail transit with higher efficiency and lower memory occupancy. Our model is optimized in an adversarial learning manner and includes (1) a generator network including gated temporal conventional networks (TCN) and weight sharing graph convolution networks (GCN) to capture structural spatiotemporal dependencies and generate predictions with a small computational burden; (2) a discriminator network including a spatial discriminator and a temporal discriminator to enhance spatial and temporal constraints of the predictions. The STG-GAN is evaluated on two datasets from Beijing Subway. Results illustrate its superiority and robustness.
{"title":"A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems","authors":"Jinlei Zhang, Hua Li, Shuxin Zhang, Lixing Yang, G. Jin, J. Qi","doi":"10.1080/03081079.2023.2203922","DOIUrl":"https://doi.org/10.1080/03081079.2023.2203922","url":null,"abstract":"ABSTRACT Most short-term passenger flow prediction methods only consider absolute errors as the optimization objective, which fails to account for spatial and temporal constraints on the predictions. To overcome these limitations, we propose a deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) to accurately predict network-wide short-term passenger flows of the urban rail transit with higher efficiency and lower memory occupancy. Our model is optimized in an adversarial learning manner and includes (1) a generator network including gated temporal conventional networks (TCN) and weight sharing graph convolution networks (GCN) to capture structural spatiotemporal dependencies and generate predictions with a small computational burden; (2) a discriminator network including a spatial discriminator and a temporal discriminator to enhance spatial and temporal constraints of the predictions. The STG-GAN is evaluated on two datasets from Beijing Subway. Results illustrate its superiority and robustness.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"694 - 721"},"PeriodicalIF":2.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44669612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-09DOI: 10.1080/03081079.2023.2206654
Taoli Yang, Zhaowen Li, Jinjin Li
ABSTRACT Attribute reduction for set-valued data commonly took into account the distance or similarity between attribute values. However, little attention has been paid to the problem that sample labels can affect attribute reduction. This paper studies the attribute reduction for set-valued data based on prediction label. Firstly, the prediction label of samples in a set-valued decision information system (SVDIS) is defined. And then, the tolerance relation in an SVDIS based on prediction labels is given, which can distinguish samples not only by the distance between the attribute values, but also by the prediction labels. As a result, some related concepts have been redefined. Moreover, attribute reduction algorithms in an SVDIS based on dependence and decision error rate are designed. Eventually, experimental analysis on real data sets indicates that the designed algorithms can effectively reduce the number of attributes, and improve the classification accuracy in most cases.
{"title":"Attribute reduction for set-valued data based on prediction label","authors":"Taoli Yang, Zhaowen Li, Jinjin Li","doi":"10.1080/03081079.2023.2206654","DOIUrl":"https://doi.org/10.1080/03081079.2023.2206654","url":null,"abstract":"ABSTRACT Attribute reduction for set-valued data commonly took into account the distance or similarity between attribute values. However, little attention has been paid to the problem that sample labels can affect attribute reduction. This paper studies the attribute reduction for set-valued data based on prediction label. Firstly, the prediction label of samples in a set-valued decision information system (SVDIS) is defined. And then, the tolerance relation in an SVDIS based on prediction labels is given, which can distinguish samples not only by the distance between the attribute values, but also by the prediction labels. As a result, some related concepts have been redefined. Moreover, attribute reduction algorithms in an SVDIS based on dependence and decision error rate are designed. Eventually, experimental analysis on real data sets indicates that the designed algorithms can effectively reduce the number of attributes, and improve the classification accuracy in most cases.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"745 - 775"},"PeriodicalIF":2.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49173864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1080/03081079.2023.2199991
M. Gull, Omar Arif
Computer vision tasks rely heavily on a huge amount of training data for classification, but in everyday situations, it is impossible to assemble a large amount of training data. Zero-shot learning (ZSL) is a promising domain for the applications in which we have no labeled data available for novel classes. It aims to recognize those unseen classes, by transferring semantic information from seen to unseen classes. In this paper, we propose a generative approach for generalized ZSL that combines the strength of Conditional Variational Autoencoder (CVAE) and Conditional Generative Adversarial Network (CGAN). The key to our approach is synthesizing visual features by including a Regressor that works on cycle-consistency loss, which will constrain the whole generative process. For experimental purposes, four challenging data sets, i.e. CUB, AWA1, AWA2 and SUN, are used in both conventional and generalized settings. Our proposed approach achieves significantly better results on these standard datasets in both settings.
{"title":"Generating visual representations for zero-shot learning via adversarial learning and variational autoencoders","authors":"M. Gull, Omar Arif","doi":"10.1080/03081079.2023.2199991","DOIUrl":"https://doi.org/10.1080/03081079.2023.2199991","url":null,"abstract":"Computer vision tasks rely heavily on a huge amount of training data for classification, but in everyday situations, it is impossible to assemble a large amount of training data. Zero-shot learning (ZSL) is a promising domain for the applications in which we have no labeled data available for novel classes. It aims to recognize those unseen classes, by transferring semantic information from seen to unseen classes. In this paper, we propose a generative approach for generalized ZSL that combines the strength of Conditional Variational Autoencoder (CVAE) and Conditional Generative Adversarial Network (CGAN). The key to our approach is synthesizing visual features by including a Regressor that works on cycle-consistency loss, which will constrain the whole generative process. For experimental purposes, four challenging data sets, i.e. CUB, AWA1, AWA2 and SUN, are used in both conventional and generalized settings. Our proposed approach achieves significantly better results on these standard datasets in both settings.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"636 - 651"},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49391262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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