Multitask Autoencoder Model for Recovering Human Poses

Yu, Jun; Hong, Chaoqun; Rui, Yong; Tao, Dacheng

doi:10.1109/tie.2017.2739691

Cited by 153 publications

(37 citation statements)

References 34 publications

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“…Through deep learning methods based on DNN, the obtained features can maintain certain invariance and contain higher-level semantic information, which effectively narrow the gap between the bottom features and the high-level semantics [47,48]. It is worthwhile exploring a specifically HS pansharpening method based on DNN that is practical and efficient to the data sets [49][50][51][52]. In this paper, we propose an unsupervised deep learning pansharpening method based on SCAAE to achieve feature extraction.…”

Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Zhong

Lei

et al. 2019

Remote Sensing

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Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

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Section: Proposed Methodsmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Zhong

Lei

et al. 2019

Remote Sensing

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“…In order to build a deep neural network, we apply SAEs model which consists of multiple layers of sparse AutoEncoders to extract features [40,41]. An AutoEncoder (AE) has three layers: input layer, hidden layer, and output layer.…”

Section: Sae-based Malware Detectionmentioning

confidence: 99%

Malware Detection Based on Deep Learning of Behavior Graphs

Xiao

Lin

Sun

et al. 2019

Mathematical Problems in Engineering

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The Internet of Things (IoT) provides various benefits, which makes smart device even closer. With more and more smart devices in IoT, security is not a one-device affair. Many attacks targeted at traditional computers in IoT environment may also aim at other IoT devices. In this paper, we consider an approach to protect IoT devices from being attacked by local computers. In response to this issue, we propose a novel behavior-based deep learning framework (BDLF) which is built in cloud platform for detecting malware in IoT environment. In the proposed BDLF, we first construct behavior graphs to provide efficient information of malware behaviors using extracted API calls. We then use a neural network-Stacked AutoEncoders (SAEs) for extracting high-level features from behavior graphs. The layers of SAEs are inserted one after another and the last layer is connected to some added classifiers. The architecture of the SAEs is 6,000-2,000-500. The experiment results demonstrate that the proposed BDLF can learn the semantics of higher-level malicious behaviors from behavior graphs and further increase the average detection precision by 1.5%.

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“…Deep convolutional autoencoder is a powerful learning model for representation learning and has been widely used for different applications [8,20,21,22,23,24,25,9]. Variational Autoencoder (VAE) [16,26] has become a popular generative model, allowing us to formalize image generation task in the framework of probabilistic graphical models with latent variables.…”

Section: Variational Autoencodermentioning

confidence: 99%

“…We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years.…”

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Improving variational autoencoder with deep feature consistent and generative adversarial training

et al. 2019

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We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years. A generative model trained with a given image database can be useful in several ways. One is to learn the essence of a dataset and generate realistic images similar to those in the dataset from random inputs. The whole dataset is "compressed" into the learned parameters of the model, which are significantly smaller than the size of the training dataset. The other is to learn reusable feature representations from unlabeled image datasets for a variety of supervised learning tasks such as image classification.In this paper, we propose a new method to train the variational autoencoder (VAE)[16] to improve its performance. In particular, we seek to improve the quality of the generated images to make them more realistic and less blurry. To achieve this, we employ objective functions based on deep feature consistent principle [17] and generative adversarial network [18,19] instead of the problematic per-pixel loss functions. The deep feature consistent can help capture important perceptual features such as spatial correlation through the learned convolutional operations, while the adversarial training helps to produce images that reside on the manifold of natural images. We also introduce several techniques to improve the convergence of GAN training in this context. In particular, instead of directly using the generated images and the real images in pixel space, the corresponding deep features extracted from pretrained networks are used to train the generator and the discriminator network. We also propose to further relax the constraint on the output of the discriminator network to balance the image reconstruction loss and the adversarial loss. We present experimental results to show that our new method can generate face images with much clearer facial parts such as eyes, nose, mouth, teeth, ears and hair textur...

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Multitask Autoencoder Model for Recovering Human Poses

Cited by 153 publications

References 34 publications

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Malware Detection Based on Deep Learning of Behavior Graphs

Improving variational autoencoder with deep feature consistent and generative adversarial training

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