Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer i...Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.展开更多
In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different f...In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.展开更多
Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, w...Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, while extracting natural and human-understandable features without labels. In this paper we combine two highly useful classes of models, variational ladder autoencoders, and MMD variational autoencoders, to model face images. In particular, we show that we can disentangle highly meaningful and interpretable features. Furthermore, we are able to perform arithmetic operations on faces and modify faces to add or remove high level features.展开更多
In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have b...In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.展开更多
Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method ...Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.展开更多
Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of f...Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of landmarks.To address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a Variational Autoencoder framework.Besides,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage.Moreover,we implement an identity preservation loss to further enhance the representation ability of identity factor.To the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.展开更多
基金National Natural Science Foundation of China(61976209,62020106015,U21A20388)in part by the CAS International Collaboration Key Project(173211KYSB20190024)in part by the Strategic Priority Research Program of CAS(XDB32040000)。
文摘Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
文摘In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.
文摘Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, while extracting natural and human-understandable features without labels. In this paper we combine two highly useful classes of models, variational ladder autoencoders, and MMD variational autoencoders, to model face images. In particular, we show that we can disentangle highly meaningful and interpretable features. Furthermore, we are able to perform arithmetic operations on faces and modify faces to add or remove high level features.
文摘In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.
基金Supported by National Natural Science Foundation of China(41804126,41604107).
文摘Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.
基金Supported by the National Natural Science Foundation of China(61210007).
文摘Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of landmarks.To address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a Variational Autoencoder framework.Besides,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage.Moreover,we implement an identity preservation loss to further enhance the representation ability of identity factor.To the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.
