The resulting basis ows are found to to be indicative of action specic image regions and therefore allow for an informed sampling of key points . We also present a generative model that allows for characterizing joint distributions of regions of interest and a human actions . In practical experiments , we determine correspondences between regions of interest that were auto- matically learned from videos and manually annotated locations of hu- man body parts available from independent benchmark image data sets . Unquestionably , reading behavior has exponential power . It is a generative process in that it gives the learner an increment of power to learn something else . And the greater the mastery over the process , the greater the degree of generative ability the individual has at his disposal . The audio signal is modelled as a superposition of quasi-sinusoidal signals generated by a linear dynamical system . The advantage of our “ generative ” perspective is that it allows a full Bayesian treatment of the problem ; e.g. one can perform the analysis while arbitrary chunks of sample values are missing or model parameters are unknown . To perform audio restoration , we derive an expectation-maximisation ( EM ) algorithm that infers the expectations of missing samples and maximum a-posteriori model parameters . By restricting the self - attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice , despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks . While conceptually simple , our generative models significantly outperform the current state of the art in image generation on ImageNet , improving the best published negative log - likelihood on ImageNet from 3.83 to 3.77 . We also present results on image super - resolution with a large magnification ratio , applying an encoder - decoder configuration of our architecture . [ reference ] . Since the RNNG is a generative model , it attempts to maximize , the joint distribution of strings and trees , defined as In other words , is defined as a product of local probabilities , conditioned on all past actions . The joint probability estimate can be used for both phrase - structure parsing ( finding ) and language modeling ( finding by marginalizing over the set of possible parses for ) . In related work , adversarial learning has been explored for generative tasks . The Generative Adversarial Network ( GAN ) method is a generative deep model that pits two networks against one another : a generative model G that captures the data distribution and a discriminative model D that distinguishes between samples drawn from G and images drawn from the training data by predicting a binary label . Regarding the loss as artificial noise , this study designs a serial denoising autoencoder ( SDAE ) composed of two neural network filters , utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation ( ES-MDA ) , and improves the overall history matching performance . As a training dataset of the SDAE , the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise : salt and pepper noise and Gaussian noise . The SDAE learns how to eliminate the noise and restore the clean reservoir models . Through large-scale experiments to comprehensively evaluate the performance of deep feature learning models for RGB-D image/ video classification , we obtain the conclusion that RGB-D fusion methods using CNNs always outperform other selected methods ( DBNs , SDAE and LSTM ) . On the other side , since LSTM can learn from experience to classify , process and predict time series , it achieved better performances than DBN and SDAE in video classification tasks . ii ) Hyper-parameter optimization can help researchers quickly choose an initial set of hyper-parameters for a new coming classification task , thus reducing the number of trials in terms of hyper-parameter space . To address these problems , a data cleaning method based on stacked denoising autoencoder ( SDAE ) networks is proposed in this paper . SDAE networks have a strong ability to denoise and restore corrupted data and have a strong feature extraction capability . The status monitoring data of equipment under normal conditions are trained by SDAE to obtain the cleaning parameters and the reconstruction errors . Abstract In this work , we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma . At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder ( SDAE ) and for backpropagation we have used Multilayer Perceptron ( MLP ) procedure . We said this model is MLP-SDAE . This study proposes a novel benign or malignant tumor classification system , which comprises intensity inhomogeneity correction and stacked denoising autoencoder ( SDAE ) , and it is suitable for small-size dataset . A classifier is established by extracting features in the multilayer training of SDAE ; automatic analysis of imaging features by the deep learning algorithm is applied on image classification , thus allowing the system to have high efficiency and robust distinguishing . In this study , two kinds of dataset ( private data and public data ) are used for deep learning models training . Models based on frequent words and jihadist language intensity are used , and the results compared . Our main conclusion is that SDAE approaches , while clearly discovering structure in document-word matrices , do not yet provide a natural interpretation strategy , limiting their practical usefulness . In contrast , SVD approaches provide interpretable models , primarily because of the coupling between document and word variation patterns . In this paper , we propose a sentiment-aware review feature mapping framework for cross-domain recommendation , called SARFM . The proposed SARFM framework applies deep learning algorithm SDAE ( Stacked Denoising Autoencoders ) to model the Sentiment-Aware Review Feature ( SARF ) of users , and transfers SARF via a multi-layer perceptron to capture the nonlinear mapping function across domains . We evaluate and compare our framework on a set of Amazon datasets . The invention provides a three-dimensional face recognition algorithm based on a deep learning SDAE theory . The procedure for constructing a three-dimensional face recognition algorithm model based on the deep learning SDAE theory includes the steps of constructing a deep learning SDAE model , normalizing face depth data , constructing an integral structure ( architecture ) of the network through the face depth data , allowing an Auto Encoder to perform pre-training without supervision , performing the fine tuning process with supervision , classifying and predicting results ( face recognition ) . According to the algorithm , deep learning is applied to processing and recognition of face three-dimensional depth data , the novel face recognition algorithm by combining the deep learning and depth information is provided , the problem of the prior art is avoided , and the high recognition rate is acquired . Synthetic minority over-sampling technique ( SMOTE ) is a classical approach to balance datasets , but it may give rise to such problem as noise . Stacked De-noising Auto-Encoder neural network ( SDAE ) , can effectively reduce data redundancy and noise through unsupervised layer-wise greedy learning . Aiming at the shortcomings of SMOTE algorithm when synthesizing new minority class samples , the paper proposed a Stacked De-noising Auto-Encoder neural network algorithm based on SMOTE , SMOTE-SDAE , which is aimed to deal with imbalanced data classification . Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning . We utilized stacked denoise autoencoder ( SDAE ) method to pretrain the network , which is robust to noise . In the top layer of the network , logistic regression ( LR ) approach is utilized to perform supervised fine-tuning and classification . The first group corresponds to models trained with unsupervised unordered sentences . This includes bag - of - words models such as word2vec - SkipGram , the Unigram - TFIDF model , the Paragraph Vector model , the Sequential Denoising Auto - Encoder ( SDAE ) and the SIF model , all trained on the Toronto book corpus . The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) . Subsequent layers use the features ( after activation ) from the previous layers as training input . Experiments have been carried out with other deep representation learning tools — deep dictionary learning , stacked denoising autoencoder , deep belief network and PCANet ( a version of convolutional neural network ) . Results show that our proposed technique is better than all the said techniques , at least on the benchmark datasets ( MNIST , CIFAR-10 and SVHN ) compared on . In this paper , we introduce a deep learning based method which use meteorological data to predict sales of a Japanese chain supermarket . To be specific , our method contains a long short-term memory ( LSTM ) network and a stacked denoising autoencoder network , both of which are used to learn how sales changes with the weathers from a large amount of history data . We showed that our method gained initial success in predicting sales of some weather-sensitive products such as drinks . To address these challenges , in this paper , we present a novel S tructure- E nriched N eural N etwork ( SENN ) for network embedding . Specifically , SENN can not only capture the complex structure similar patterns observed in networks by introducing direction adjustment parameters of the transition probability , but also introduce a stacked denoise autoencoder to perform the dimension reduction for each order matrix independently . Therefore , SENN can preserve more useful structure information and make the embeddings more robust . Firstly , this paper selects 30 feature points from the 113 feature points of Candide-3 face model to characterize face , which improves the efficiency of recognition algorithm obviously without affecting the recognition accuracy . With the significant advantage of the characterization of essential features by learning a deep nonlinear network , this paper presents a stacked denoising autoencoder algorithm model based on deep learning which improves neural networks model . This algorithm conducts the unsupervised preliminary training of face depth data and the supervised training to fine-tuning the network which is better than neural network 's random initialization . First , multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains ( e.g. time frequency domain , and time frequency domain ) . Second , the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers , and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder , which is aimed at learning more robust and reliable feature representation . Finally , the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions . In the off-line training phase , this paper firstly proposes a matrix to pre-processing the raw data from a smartphone to overcome its redundancy and complexity . In a second place , this paper managed to go through a stacked denoising autoencoder neural network , resulting in an output layer with seven different indoor movement states . The trained network is then implemented on a smartphone and evaluated with online tests . Given the limited nature of parallel data for under-resourced languages , we would like to make the most of this resource for our task . We compare zero-shot learning , bilingual word embeddings , stacked denoising autoencoder representations and machine translation techniques for aspect-based CLSC . We show that models based on distributed semantics can achieve comparable results to machine translation on aspect-based CLSC . Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy , a new remote sensing image classification method inspired by deep learning is proposed , which is based on Stacked Denoising Autoencoder . First , the deep network model is built through the stacked layers of Denoising Autoencoder . Then , with noised input , the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing , characteristics are obtained in supervised learning by Back Propagation ( BP ) neural network , and the whole network is optimized by error back propagation . However , the clinical pre-processing and classification approach usually contains noise induced by camera illumination and lighting effects , resulting in the loss of information in the features that are essential for detecting disease . This chapter studied a novel strategy to design structured stacked denoising autoencoder ( SDA ) with binary high dimensional matrices of compressing sampling ( HDM ) , which is a deep learning algorithm , based on promoting linear independence between rows by reducing the number of zero singular values . The design constraints establish a SDA-CS restoration method for simultaneous feature extraction , and compressive imaging by identifying global and invariant features and is robust against illumination noise . Most similar to our work are neural network approaches which try to match the feature distributions between the domains . This was performed in an unsupervised way with mSDA chen2012marginalized by training denoising autoencoders jointly on all domains , thus implicitly obtaining a representation general enough to explain both the domain and the data . This is in contrast to our approach where we instead try to learn representations that explicitly remove domain information during the learning process . In unsupervised learning , several studies on learning invariant representations exist . Denoising autoencoders [ 9 ] , for example , learn features that are robust to noise by trying to reconstruct data from randomly perturbed input samples . Zou et al . Recently , deep neural networks ( DNNs ) have shown their superior performance in image processing and computer vision tasks , ranging from high - level recognition , semantic segmentation to low - level denoising and super - resolution . One of the early deep learning models which has been used for image denoising is the Stacked Denoising Auto - encoders ( SdA ) . It is an extension of the stacked auto - encoder and was originally designed for unsupervised feature learning . Furthermore , with the help of unsupervised pre - training , we can get good feature representation which guides the learning towards basins of attraction of minima that support better generalisation from the training data . Usually , these deep models have two stages in learning : the first stage performs model initialisation via unsupervised learning ( i.e. , the restricted Boltzmann machine or stacked denoising auto - encoders ) to make the model catch the input data distribution ; the second stage involves a fine tuning of the initialised model via supervised learning with back - propagation . The novelty of our deep learning models lies in the first layer initialisation , where the input raw features are high dimensional and sparse binary features converted from the raw categorical features , which makes it hard to train traditional DNNs in large scale . section : Combining DANN with Denoising Autoencoders We now investigate on whether the DANN algorithm can improve on the representation learned by the state - of - the - art Marginalized Stacked Denoising Autoencoders ( mSDA ) proposed by [ reference ] . In brief , mSDA is an unsupervised algorithm that learns a new robust feature representation of the training samples . There exists a large body of work on unsupervised representation learning . Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] . This paper adapts a StyleGAN model for speech generation with minimal or no conditioning on text . StyleGAN is a multi-scale convolutional GAN capable of hierarchically capturing data structure and latent variation on multiple spatial ( or temporal ) levels . The model has previously achieved impressive results on facial image generation , and it is appealing to audio applications due to similar multi-level structures present in the data . The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content , both in 2D and in 3D . Generative Adversarial Networks ( GANs ) and style control mechanisms , such as Adaptive Instance Normalization ( AdaIN ) , have proved particularly effective in this context , culminating in the state-of-the-art StyleGAN architecture . While such models are able to learn diverse distributions , provided a sufficiently large training set , they are not well-suited for scenarios where the distribution of the training data exhibits a multi-modal behavior . In this paper , we introduce uMM-GAN , a novel architecture designed to better model multi-modal distributions , in an unsupervised fashion . Building upon the StyleGAN architecture , our network learns multiple modes , in a completely unsupervised manner , and combines them using a set of learned weights . We demonstrate that this approach is capable of effectively approximating a complex distribution as a superposition of multiple simple ones . The style-based GAN architecture ( StyleGAN ) yields state-of-the-art results in data-driven unconditional generative image modeling . We expose and analyze several of its characteristic artifacts , and propose changes in both model architecture and training methods to address them . The trained networks achieve state of the art results in binary classification in the { \it FaceForensics++ } dataset and competitive performance in other tasks using a significantly reduced number of parameters and small resolution input images . Additionally , we examine how well the proposed architecture can detect fully generated images using faces from the recently proposed PGAN and StyleGAN methods . We show that this task is easier to learn than detecting manipulated images and that for both cases there is only a small drop of performance when the network is trained using more than one manipulation technique in the training data . It is a general architecture that can leverage recent improvements on GAN training procedures . We designed two autoencoders : one based on a MLP encoder , and another based on a StyleGAN generator , which we call StyleALAE . We verify the disentanglement properties of both architectures . Practical issues and challenges arising from the retraining process are discussed . Tests and validation results are presented and a comparative analysis of several different re-trained StyleGAN weightings is provided . The role of this tool in building large , scalable datasets of synthetic facial data is also discussed . In this paper , we present a layout- and style-based architecture for generative adversarial networks ( termed LostGANs ) that can be trained end-to-end to generate images from reconfigurable layout and style . Inspired by the vanilla StyleGAN , the proposed LostGAN consists of two new components : ( i ) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images , and ( ii ) learning object instance-specific layout-aware feature normalization ( ISLA-Norm ) in the generator to realize multi-object style generation . In experiments , the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained . In this paper , we propose a framework capable of generating face images that fall into the same distribution as that of a given one-shot example . We leverage a pre-trained StyleGAN model that already learned the generic face distribution . Given the one-shot target , we develop an iterative optimization scheme that rapidly adapts the weights of the model to shift the output 's high-level distribution to the target 's . We use simple feed-forward encoder and decoder networks , making our model an attractive candidate for applications where the encoding and/or decoding speed is critical . Additionally , VQ-VAE requires sampling an autoregressive model only in the compressed latent space , which is an order of magnitude faster than sampling in the pixel space , especially for large images . We demonstrate that a multi-scale hierarchical organization of VQ-VAE , augmented with powerful priors over the latent codes , is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet , while not suffering from GAN 's known shortcomings such as mode collapse and lack of diversity . Previous studies have shown that VQ-VAE can generate high-quality VC syntheses when it is paired with a powerful decoder . However , in a conventional VQ-VAE , adjacent atoms in the embedding dictionary can represent entirely different phonetic content . Therefore , the VC syntheses can have mispronunciations and distortions whenever the output of the encoder is quantized to an atom representing entirely different phonetic content . In this paper , we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations . We combine both perspectives of Vector Quantized-Variational AutoEncoders ( VQ-VAE ) and classical denoising regularization methods of neural networks . We interpret quantizers as regularizers that constrain latent representations while fostering a similarity-preserving mapping at the encoder . Here , we propose a Vector Quantized Variational Autoencoder ( VQ-VAE ) neural < inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula > model that is both more efficient and more interpretable than the DAR . This model has two stages : one uses the VQ-VAE framework to learn a latent code for the < inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula > contour of each linguistic unit , and other learns to map from linguistic features to latent codes . In contrast to the DAR and RNN , which process the input linguistic features frame-by-frame , the new model converts one linguistic feature vector into one latent code for each linguistic unit . Moreover , the system should also balance the discrimination score ABX , the bit-rate compression rate , and the naturalness and the intelligibility of the constructed voice . To tackle these problems and achieve the best trade-off , we utilize a vector quantized variational autoencoder ( VQ-VAE ) and a multi-scale codebook-to-spectrogram ( Code2Spec ) inverter trained by mean square error and adversarial loss . The VQ-VAE extracts the speech to a latent space , forces itself to map it into the nearest codebook and produces compressed representation . The system also needs to balance the codebook performance between the ABX error rate and the bitrate compression rate . Our main contribution here is we proposed Transformer-based VQ-VAE for unsupervised unit discovery and Transformer-based inverter for the speech synthesis given the extracted codebook . Additionally , we also explored several regularization methods to improve performance even further . For recommendation services with a large number of users , the most powerful models may require an impractical amount of space to store the large dense vectors encoding each of the users ’ tastes . Combining ideas from auto-encoder-based recommender systems , neural discrete representation learning ( VQ-VAE ) , and product quantization ( PQ ) , we propose PQ-VAE , a recommendation model that learns compact , discrete embeddings at only a small cost in accuracy .
The resulting basis ows are found to to be indicative of action specic image regions and therefore allow for an informed sampling of key points . We also present a generative model that allows for characterizing joint distributions of regions of interest and a human actions . In practical experiments , we determine correspondences between regions of interest that were auto- matically learned from videos and manually annotated locations of hu- man body parts available from independent benchmark image data sets .
Unquestionably , reading behavior has exponential power . It is a generative process in that it gives the learner an increment of power to learn something else . And the greater the mastery over the process , the greater the degree of generative ability the individual has at his disposal .
The audio signal is modelled as a superposition of quasi-sinusoidal signals generated by a linear dynamical system . The advantage of our “ generative ” perspective is that it allows a full Bayesian treatment of the problem ; e.g. one can perform the analysis while arbitrary chunks of sample values are missing or model parameters are unknown . To perform audio restoration , we derive an expectation-maximisation ( EM ) algorithm that infers the expectations of missing samples and maximum a-posteriori model parameters .
By restricting the self - attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice , despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks . While conceptually simple , our generative models significantly outperform the current state of the art in image generation on ImageNet , improving the best published negative log - likelihood on ImageNet from 3.83 to 3.77 . We also present results on image super - resolution with a large magnification ratio , applying an encoder - decoder configuration of our architecture .
[ reference ] . Since the RNNG is a generative model , it attempts to maximize , the joint distribution of strings and trees , defined as In other words , is defined as a product of local probabilities , conditioned on all past actions . The joint probability estimate can be used for both phrase - structure parsing ( finding ) and language modeling ( finding by marginalizing over the set of possible parses for ) .
In related work , adversarial learning has been explored for generative tasks . The Generative Adversarial Network ( GAN ) method is a generative deep model that pits two networks against one another : a generative model G that captures the data distribution and a discriminative model D that distinguishes between samples drawn from G and images drawn from the training data by predicting a binary label .
Regarding the loss as artificial noise , this study designs a serial denoising autoencoder ( SDAE ) composed of two neural network filters , utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation ( ES-MDA ) , and improves the overall history matching performance . As a training dataset of the SDAE , the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise : salt and pepper noise and Gaussian noise . The SDAE learns how to eliminate the noise and restore the clean reservoir models .
Through large-scale experiments to comprehensively evaluate the performance of deep feature learning models for RGB-D image/ video classification , we obtain the conclusion that RGB-D fusion methods using CNNs always outperform other selected methods ( DBNs , SDAE and LSTM ) . On the other side , since LSTM can learn from experience to classify , process and predict time series , it achieved better performances than DBN and SDAE in video classification tasks . ii ) Hyper-parameter optimization can help researchers quickly choose an initial set of hyper-parameters for a new coming classification task , thus reducing the number of trials in terms of hyper-parameter space .
To address these problems , a data cleaning method based on stacked denoising autoencoder ( SDAE ) networks is proposed in this paper . SDAE networks have a strong ability to denoise and restore corrupted data and have a strong feature extraction capability . The status monitoring data of equipment under normal conditions are trained by SDAE to obtain the cleaning parameters and the reconstruction errors .
Abstract In this work , we form stacked denoising auto encoder model which is recognized few feasible genes mediating human lung adenocarcinoma . At first we have trained the data for feature selection by using Stacked Denoising Auto-encoder ( SDAE ) and for backpropagation we have used Multilayer Perceptron ( MLP ) procedure . We said this model is MLP-SDAE .
This study proposes a novel benign or malignant tumor classification system , which comprises intensity inhomogeneity correction and stacked denoising autoencoder ( SDAE ) , and it is suitable for small-size dataset . A classifier is established by extracting features in the multilayer training of SDAE ; automatic analysis of imaging features by the deep learning algorithm is applied on image classification , thus allowing the system to have high efficiency and robust distinguishing . In this study , two kinds of dataset ( private data and public data ) are used for deep learning models training .
Models based on frequent words and jihadist language intensity are used , and the results compared . Our main conclusion is that SDAE approaches , while clearly discovering structure in document-word matrices , do not yet provide a natural interpretation strategy , limiting their practical usefulness . In contrast , SVD approaches provide interpretable models , primarily because of the coupling between document and word variation patterns .
In this paper , we propose a sentiment-aware review feature mapping framework for cross-domain recommendation , called SARFM . The proposed SARFM framework applies deep learning algorithm SDAE ( Stacked Denoising Autoencoders ) to model the Sentiment-Aware Review Feature ( SARF ) of users , and transfers SARF via a multi-layer perceptron to capture the nonlinear mapping function across domains . We evaluate and compare our framework on a set of Amazon datasets .
The invention provides a three-dimensional face recognition algorithm based on a deep learning SDAE theory . The procedure for constructing a three-dimensional face recognition algorithm model based on the deep learning SDAE theory includes the steps of constructing a deep learning SDAE model , normalizing face depth data , constructing an integral structure ( architecture ) of the network through the face depth data , allowing an Auto Encoder to perform pre-training without supervision , performing the fine tuning process with supervision , classifying and predicting results ( face recognition ) . According to the algorithm , deep learning is applied to processing and recognition of face three-dimensional depth data , the novel face recognition algorithm by combining the deep learning and depth information is provided , the problem of the prior art is avoided , and the high recognition rate is acquired .
Synthetic minority over-sampling technique ( SMOTE ) is a classical approach to balance datasets , but it may give rise to such problem as noise . Stacked De-noising Auto-Encoder neural network ( SDAE ) , can effectively reduce data redundancy and noise through unsupervised layer-wise greedy learning . Aiming at the shortcomings of SMOTE algorithm when synthesizing new minority class samples , the paper proposed a Stacked De-noising Auto-Encoder neural network algorithm based on SMOTE , SMOTE-SDAE , which is aimed to deal with imbalanced data classification .
Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning . We utilized stacked denoise autoencoder ( SDAE ) method to pretrain the network , which is robust to noise . In the top layer of the network , logistic regression ( LR ) approach is utilized to perform supervised fine-tuning and classification .
The first group corresponds to models trained with unsupervised unordered sentences . This includes bag - of - words models such as word2vec - SkipGram , the Unigram - TFIDF model , the Paragraph Vector model , the Sequential Denoising Auto - Encoder ( SDAE ) and the SIF model , all trained on the Toronto book corpus . The second group consists of models trained with unsupervised ordered sentences such as FastSent and SkipThought ( also trained on the Toronto book corpus ) .
Subsequent layers use the features ( after activation ) from the previous layers as training input . Experiments have been carried out with other deep representation learning tools — deep dictionary learning , stacked denoising autoencoder , deep belief network and PCANet ( a version of convolutional neural network ) . Results show that our proposed technique is better than all the said techniques , at least on the benchmark datasets ( MNIST , CIFAR-10 and SVHN ) compared on .
In this paper , we introduce a deep learning based method which use meteorological data to predict sales of a Japanese chain supermarket . To be specific , our method contains a long short-term memory ( LSTM ) network and a stacked denoising autoencoder network , both of which are used to learn how sales changes with the weathers from a large amount of history data . We showed that our method gained initial success in predicting sales of some weather-sensitive products such as drinks .
To address these challenges , in this paper , we present a novel S tructure- E nriched N eural N etwork ( SENN ) for network embedding . Specifically , SENN can not only capture the complex structure similar patterns observed in networks by introducing direction adjustment parameters of the transition probability , but also introduce a stacked denoise autoencoder to perform the dimension reduction for each order matrix independently . Therefore , SENN can preserve more useful structure information and make the embeddings more robust .
Firstly , this paper selects 30 feature points from the 113 feature points of Candide-3 face model to characterize face , which improves the efficiency of recognition algorithm obviously without affecting the recognition accuracy . With the significant advantage of the characterization of essential features by learning a deep nonlinear network , this paper presents a stacked denoising autoencoder algorithm model based on deep learning which improves neural networks model . This algorithm conducts the unsupervised preliminary training of face depth data and the supervised training to fine-tuning the network which is better than neural network 's random initialization .
First , multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains ( e.g. time frequency domain , and time frequency domain ) . Second , the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers , and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder , which is aimed at learning more robust and reliable feature representation . Finally , the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions .
In the off-line training phase , this paper firstly proposes a matrix to pre-processing the raw data from a smartphone to overcome its redundancy and complexity . In a second place , this paper managed to go through a stacked denoising autoencoder neural network , resulting in an output layer with seven different indoor movement states . The trained network is then implemented on a smartphone and evaluated with online tests .
Given the limited nature of parallel data for under-resourced languages , we would like to make the most of this resource for our task . We compare zero-shot learning , bilingual word embeddings , stacked denoising autoencoder representations and machine translation techniques for aspect-based CLSC . We show that models based on distributed semantics can achieve comparable results to machine translation on aspect-based CLSC .
Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy , a new remote sensing image classification method inspired by deep learning is proposed , which is based on Stacked Denoising Autoencoder . First , the deep network model is built through the stacked layers of Denoising Autoencoder . Then , with noised input , the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing , characteristics are obtained in supervised learning by Back Propagation ( BP ) neural network , and the whole network is optimized by error back propagation .
However , the clinical pre-processing and classification approach usually contains noise induced by camera illumination and lighting effects , resulting in the loss of information in the features that are essential for detecting disease . This chapter studied a novel strategy to design structured stacked denoising autoencoder ( SDA ) with binary high dimensional matrices of compressing sampling ( HDM ) , which is a deep learning algorithm , based on promoting linear independence between rows by reducing the number of zero singular values . The design constraints establish a SDA-CS restoration method for simultaneous feature extraction , and compressive imaging by identifying global and invariant features and is robust against illumination noise .
Most similar to our work are neural network approaches which try to match the feature distributions between the domains . This was performed in an unsupervised way with mSDA chen2012marginalized by training denoising autoencoders jointly on all domains , thus implicitly obtaining a representation general enough to explain both the domain and the data . This is in contrast to our approach where we instead try to learn representations that explicitly remove domain information during the learning process .
In unsupervised learning , several studies on learning invariant representations exist . Denoising autoencoders [ 9 ] , for example , learn features that are robust to noise by trying to reconstruct data from randomly perturbed input samples . Zou et al .
Recently , deep neural networks ( DNNs ) have shown their superior performance in image processing and computer vision tasks , ranging from high - level recognition , semantic segmentation to low - level denoising and super - resolution . One of the early deep learning models which has been used for image denoising is the Stacked Denoising Auto - encoders ( SdA ) . It is an extension of the stacked auto - encoder and was originally designed for unsupervised feature learning .
Furthermore , with the help of unsupervised pre - training , we can get good feature representation which guides the learning towards basins of attraction of minima that support better generalisation from the training data . Usually , these deep models have two stages in learning : the first stage performs model initialisation via unsupervised learning ( i.e. , the restricted Boltzmann machine or stacked denoising auto - encoders ) to make the model catch the input data distribution ; the second stage involves a fine tuning of the initialised model via supervised learning with back - propagation . The novelty of our deep learning models lies in the first layer initialisation , where the input raw features are high dimensional and sparse binary features converted from the raw categorical features , which makes it hard to train traditional DNNs in large scale .
section : Combining DANN with Denoising Autoencoders We now investigate on whether the DANN algorithm can improve on the representation learned by the state - of - the - art Marginalized Stacked Denoising Autoencoders ( mSDA ) proposed by [ reference ] . In brief , mSDA is an unsupervised algorithm that learns a new robust feature representation of the training samples .
There exists a large body of work on unsupervised representation learning . Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] .
This paper adapts a StyleGAN model for speech generation with minimal or no conditioning on text . StyleGAN is a multi-scale convolutional GAN capable of hierarchically capturing data structure and latent variation on multiple spatial ( or temporal ) levels . The model has previously achieved impressive results on facial image generation , and it is appealing to audio applications due to similar multi-level structures present in the data .
The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content , both in 2D and in 3D . Generative Adversarial Networks ( GANs ) and style control mechanisms , such as Adaptive Instance Normalization ( AdaIN ) , have proved particularly effective in this context , culminating in the state-of-the-art StyleGAN architecture . While such models are able to learn diverse distributions , provided a sufficiently large training set , they are not well-suited for scenarios where the distribution of the training data exhibits a multi-modal behavior .
In this paper , we introduce uMM-GAN , a novel architecture designed to better model multi-modal distributions , in an unsupervised fashion . Building upon the StyleGAN architecture , our network learns multiple modes , in a completely unsupervised manner , and combines them using a set of learned weights . We demonstrate that this approach is capable of effectively approximating a complex distribution as a superposition of multiple simple ones .
The style-based GAN architecture ( StyleGAN ) yields state-of-the-art results in data-driven unconditional generative image modeling . We expose and analyze several of its characteristic artifacts , and propose changes in both model architecture and training methods to address them .
The trained networks achieve state of the art results in binary classification in the { \it FaceForensics++ } dataset and competitive performance in other tasks using a significantly reduced number of parameters and small resolution input images . Additionally , we examine how well the proposed architecture can detect fully generated images using faces from the recently proposed PGAN and StyleGAN methods . We show that this task is easier to learn than detecting manipulated images and that for both cases there is only a small drop of performance when the network is trained using more than one manipulation technique in the training data .
It is a general architecture that can leverage recent improvements on GAN training procedures . We designed two autoencoders : one based on a MLP encoder , and another based on a StyleGAN generator , which we call StyleALAE . We verify the disentanglement properties of both architectures .
Practical issues and challenges arising from the retraining process are discussed . Tests and validation results are presented and a comparative analysis of several different re-trained StyleGAN weightings is provided . The role of this tool in building large , scalable datasets of synthetic facial data is also discussed .
In this paper , we present a layout- and style-based architecture for generative adversarial networks ( termed LostGANs ) that can be trained end-to-end to generate images from reconfigurable layout and style . Inspired by the vanilla StyleGAN , the proposed LostGAN consists of two new components : ( i ) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images , and ( ii ) learning object instance-specific layout-aware feature normalization ( ISLA-Norm ) in the generator to realize multi-object style generation . In experiments , the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained .
In this paper , we propose a framework capable of generating face images that fall into the same distribution as that of a given one-shot example . We leverage a pre-trained StyleGAN model that already learned the generic face distribution . Given the one-shot target , we develop an iterative optimization scheme that rapidly adapts the weights of the model to shift the output 's high-level distribution to the target 's .
We use simple feed-forward encoder and decoder networks , making our model an attractive candidate for applications where the encoding and/or decoding speed is critical . Additionally , VQ-VAE requires sampling an autoregressive model only in the compressed latent space , which is an order of magnitude faster than sampling in the pixel space , especially for large images . We demonstrate that a multi-scale hierarchical organization of VQ-VAE , augmented with powerful priors over the latent codes , is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet , while not suffering from GAN 's known shortcomings such as mode collapse and lack of diversity .
Previous studies have shown that VQ-VAE can generate high-quality VC syntheses when it is paired with a powerful decoder . However , in a conventional VQ-VAE , adjacent atoms in the embedding dictionary can represent entirely different phonetic content . Therefore , the VC syntheses can have mispronunciations and distortions whenever the output of the encoder is quantized to an atom representing entirely different phonetic content .
In this paper , we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations . We combine both perspectives of Vector Quantized-Variational AutoEncoders ( VQ-VAE ) and classical denoising regularization methods of neural networks . We interpret quantizers as regularizers that constrain latent representations while fostering a similarity-preserving mapping at the encoder .
Here , we propose a Vector Quantized Variational Autoencoder ( VQ-VAE ) neural < inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula > model that is both more efficient and more interpretable than the DAR . This model has two stages : one uses the VQ-VAE framework to learn a latent code for the < inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula > contour of each linguistic unit , and other learns to map from linguistic features to latent codes . In contrast to the DAR and RNN , which process the input linguistic features frame-by-frame , the new model converts one linguistic feature vector into one latent code for each linguistic unit .
Moreover , the system should also balance the discrimination score ABX , the bit-rate compression rate , and the naturalness and the intelligibility of the constructed voice . To tackle these problems and achieve the best trade-off , we utilize a vector quantized variational autoencoder ( VQ-VAE ) and a multi-scale codebook-to-spectrogram ( Code2Spec ) inverter trained by mean square error and adversarial loss . The VQ-VAE extracts the speech to a latent space , forces itself to map it into the nearest codebook and produces compressed representation .
The system also needs to balance the codebook performance between the ABX error rate and the bitrate compression rate . Our main contribution here is we proposed Transformer-based VQ-VAE for unsupervised unit discovery and Transformer-based inverter for the speech synthesis given the extracted codebook . Additionally , we also explored several regularization methods to improve performance even further .
For recommendation services with a large number of users , the most powerful models may require an impractical amount of space to store the large dense vectors encoding each of the users ’ tastes . Combining ideas from auto-encoder-based recommender systems , neural discrete representation learning ( VQ-VAE ) , and product quantization ( PQ ) , we propose PQ-VAE , a recommendation model that learns compact , discrete embeddings at only a small cost in accuracy .
Learning Spatial Interest Regions from Videos to Inform Action Recognition in Still Images Acquisition of a Reading Repertoire. Probabilistic phase vocoder and its application to interpolation of missing values in audio signals Image Transformer What Do Recurrent Neural Network Grammars Learn About Syntax? Adversarial Discriminative Domain Adaptation History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA Feature learning for RGB-D data Cleaning Method for Status Monitoring Data of Power Equipment Based on Stacked Denoising Autoencoders Identification of Cancer Mediating Biomarkers using Stacked Denoising Autoencoder Model - An Application on Human Lung Data Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network? Comparing SVD and SDAE for Analysis of Islamist Forum Postings SARFM: A Sentiment-Aware Review Feature Mapping Approach for Cross-Domain Recommendation Three-dimensional face recognition algorithm based on deep learning SDAE theory and application thereof in field of finance An Imbalanced Data Classification Algorithm of De-noising Auto- Encoder Neural Network Based on SMOTE Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Greedy deep transform learning Food Sales Prediction with Meteorological Data - A Case Study of a Japanese Chain Supermarket A Structure-Enriched Neural Network for network embedding Research of 3D face recognition algorithm based on deep learning stacked denoising autoencoder theory Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder Indoor Motion State Recognition using Denoising Autoencoders on Smartphone Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification Remote Sensing Image Classification Based on Stacked Denoising Autoencoder A Stacked Denoising Autoencoder Compression Sampling Method for Compressing Microscopic Images The Variational Fair Autoencoder Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Domain-Adversarial Training of Neural Networks InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Conditional Spoken Digit Generation with StyleGAN Unsupervised K-modal styled content generation Analyzing and Improving the Image Quality of StyleGAN Using Fully Convolutional Neural Networks to detect manipulated images in videos Adversarial Latent Autoencoders Re-Training StyleGAN - A First Step Towards Building Large, Scalable Synthetic Facial Datasets Image Synthesis From Reconfigurable Layout and Style One-Shot Domain Adaptation for Face Generation Generating Diverse High-Fidelity Images with VQ-VAE-2 Group Latent Embedding for Vector Quantized Variational Autoencoder in Non-Parallel Voice Conversion Vector Quantization-Based Regularization for Autoencoders A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural $F_0$ Model for Statistical Parametric Speech Synthesis VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019 Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge PQ-VAE: Efficient Recommendation Using Quantized Embeddings
723f879b93097c22ffa4fe6b587d3a070a67136b 94cc16236fb7aa93fb627b481770bc64b5c065c2 0a5ddbdc53e42fa0cbd925a4857838a96aa7e1bb 5f9728ed3560d33e89f4b06536c5db8b5df222d8 8802dc6ed81219b3e1037374a26d2f2c6bcd0fb7 40efa07f22fb2be42fe4b5e987c46ecd17f0352d cd43d5c7cd56d9eea5fca0fea0aefcce75a1f12d e6ba1a2533d8f93ef80a360cf6857d850e33be0a 547ed992fddf33562741772e417e903636452e92 e687b4f5841ed5af4a5547281d0fd771fc8b37ed a6049324cf1460cbfad695ed86bc027b9f862c13 a738bc08c25ded25b69632f8b01658abd2153ef4 eeadd2c922df8c78458c71592e04c1e63dafa294 cc46c2f0648171ec369643a6646c873d852c66e9 bb5db1311c0b2ee46026f613bcd6f0f778bc7c1b 93fb68d30ad7955f0781db3c3f50b613cca9ea6d 5f89e7f6c04d39b4f900b21d08b54e0d87a7faf0 ca0495a2aadb00f4f2d874c2af53ec840c1f3d4d 97499a55cdb33daf86cca052eaf72688b4b76beb 007b417016961264e17d917aeb936d99d9045f1b 27fca2a7c2d565be983b4a786194e92bdc3d6ac7 f1a798c0528afc6bd903e06a2ed8da05c717078d 452e61159eee14d0adea7708bb91f45b808bce0e 6156239ceab6ab5daaaf51ef27cd5163249ff2fe f3e3d1f86a534a3654d0ee263142e44f4e2c61e9 9600321cf392334682edf1b3efc897a7ba392d47 6708c62e1234aa1de38b52dddef51198f1c718fa 5e0675694ccaed1beeae9607ab90935f412ba019 03e504fd71cdc80ebcc16798e951ece979eb7e0e 342c11f5cea38a7d628e99febe82c1c6aad1027f 6be216d93421bf19c1659e7721241ae73d483baf d2f7e74336baddc6e5e1ac607ad3e33cb24068bd c08f5ea724363403831b255ab4020ce57f28321a 968379e8dd6638b15519be82652a2273531ebf2f 50970f392a76ced3054703a21d581377f1cc1086 e92a26fc08e149102860b7af92b686cedf9defce 613bdd660949b2969c68e0c02f3190b500418532
["Geography","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Geology"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science","Geology"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Engineering"] ["Computer Science"]
2014 1976 2005 2018 2017 2017 2019 2017 2017 2020 2018 2015 2018 2015 2016 2016 2017 2017 2017 2019 2016 2020 2018 2016 2017 2020 2016 2014 2016 2016 2016 2016 2020 2020 2019 2019 2020 2020 2019 2020 2019 2019 2020 2020 2019 2020 2019
LWA EUSIPCO ICML EACL 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICDM Workshops WISE (2) EMNLP ICIP DMBD COLING ICLR NIPS ArXiv ECIR J. Mach. Learn. Res. NIPS NeurIPS INTERSPEECH AAAI RecSys (Late-Breaking Results)
https://api.semanticscholar.org/723f879b93097c22ffa4fe6b587d3a070a67136b https://api.semanticscholar.org/94cc16236fb7aa93fb627b481770bc64b5c065c2 https://api.semanticscholar.org/0a5ddbdc53e42fa0cbd925a4857838a96aa7e1bb https://www.semanticscholar.org/paper/1db9bd18681b96473f3c82b21edc9240b44dc329 https://www.semanticscholar.org/paper/fce10a1a9727cbda33d44b62409e303f1009417a https://www.semanticscholar.org/paper/345afa0e85cb2f5cb438ae44027499ff2c392409 https://api.semanticscholar.org/5f9728ed3560d33e89f4b06536c5db8b5df222d8 https://api.semanticscholar.org/8802dc6ed81219b3e1037374a26d2f2c6bcd0fb7 https://api.semanticscholar.org/40efa07f22fb2be42fe4b5e987c46ecd17f0352d https://api.semanticscholar.org/cd43d5c7cd56d9eea5fca0fea0aefcce75a1f12d https://api.semanticscholar.org/e6ba1a2533d8f93ef80a360cf6857d850e33be0a https://api.semanticscholar.org/547ed992fddf33562741772e417e903636452e92 https://api.semanticscholar.org/e687b4f5841ed5af4a5547281d0fd771fc8b37ed https://api.semanticscholar.org/a6049324cf1460cbfad695ed86bc027b9f862c13 https://api.semanticscholar.org/a738bc08c25ded25b69632f8b01658abd2153ef4 https://api.semanticscholar.org/eeadd2c922df8c78458c71592e04c1e63dafa294 https://www.semanticscholar.org/paper/ee7b883e35d754ae4f71c21bb71f9f03e4ffbb2c https://api.semanticscholar.org/cc46c2f0648171ec369643a6646c873d852c66e9 https://api.semanticscholar.org/bb5db1311c0b2ee46026f613bcd6f0f778bc7c1b https://api.semanticscholar.org/93fb68d30ad7955f0781db3c3f50b613cca9ea6d https://api.semanticscholar.org/5f89e7f6c04d39b4f900b21d08b54e0d87a7faf0 https://api.semanticscholar.org/ca0495a2aadb00f4f2d874c2af53ec840c1f3d4d https://api.semanticscholar.org/97499a55cdb33daf86cca052eaf72688b4b76beb https://api.semanticscholar.org/007b417016961264e17d917aeb936d99d9045f1b https://api.semanticscholar.org/27fca2a7c2d565be983b4a786194e92bdc3d6ac7 https://api.semanticscholar.org/f1a798c0528afc6bd903e06a2ed8da05c717078d https://www.semanticscholar.org/paper/cbef7a84a53e19e019e5a05d232eb3c487c0e0c6 https://www.semanticscholar.org/paper/a6fd96a900d4130940b488863b71fd09ad41ccb9 https://www.semanticscholar.org/paper/3c6599052623542f6e21a2cc45a6fde4fb2dc374 https://www.semanticscholar.org/paper/1bc072002d97808340b312b69427baf2dc9fcb8e https://www.semanticscholar.org/paper/1d5972b32a9b5a455a6eef389de5b7fca25771ad https://www.semanticscholar.org/paper/35da0a2001eea88486a5de677ab97868c93d0824 https://api.semanticscholar.org/452e61159eee14d0adea7708bb91f45b808bce0e https://api.semanticscholar.org/6156239ceab6ab5daaaf51ef27cd5163249ff2fe https://api.semanticscholar.org/f3e3d1f86a534a3654d0ee263142e44f4e2c61e9 https://api.semanticscholar.org/9600321cf392334682edf1b3efc897a7ba392d47 https://api.semanticscholar.org/6708c62e1234aa1de38b52dddef51198f1c718fa https://api.semanticscholar.org/5e0675694ccaed1beeae9607ab90935f412ba019 https://api.semanticscholar.org/03e504fd71cdc80ebcc16798e951ece979eb7e0e https://api.semanticscholar.org/342c11f5cea38a7d628e99febe82c1c6aad1027f https://api.semanticscholar.org/6be216d93421bf19c1659e7721241ae73d483baf https://api.semanticscholar.org/d2f7e74336baddc6e5e1ac607ad3e33cb24068bd https://api.semanticscholar.org/c08f5ea724363403831b255ab4020ce57f28321a https://api.semanticscholar.org/968379e8dd6638b15519be82652a2273531ebf2f https://api.semanticscholar.org/50970f392a76ced3054703a21d581377f1cc1086 https://api.semanticscholar.org/e92a26fc08e149102860b7af92b686cedf9defce https://api.semanticscholar.org/613bdd660949b2969c68e0c02f3190b500418532