Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis. Struct. Health Monit, 2018. ,
Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother, Comput. Geosci, vol.128, pp.87-102, 2019. ,
Process monitoring using variational autoencoder for high-dimensional nonlinear processes, Eng. Appl. Artif. Intell, vol.83, pp.13-27, 2019. ,
Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring, J. Process. Control, vol.75, pp.136-155, 2019. ,
, Generative Adversarial Nets. In Advances in Neural Information Processing Systems
, , pp.2672-2680, 2014.
Abnormal Event Detection from Videos using a Two-stream Recurrent Variational Autoencoder, IEEE Trans. Cogn. Dev. Syst, vol.12, pp.30-42, 2018. ,
Neural Networks for Anomaly Detection in Crowded Scenes, IEEE Trans. Inf. Forensics Secur, vol.14, pp.1390-1399, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02318507
Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection, IEEE Access, vol.6, pp.33353-33361, 2018. ,
Additional Variational Autoencoder for Top-N Recommender Systems, IEEE Access, vol.7, pp.5707-5713, 2019. ,
Semisupervised Text Classification by Variational Autoencoder, IEEE Trans. Neural Netw. Learn. Syst, vol.31, pp.295-308, 2019. ,
Latent Space Expanded Variational Autoencoder for Sentence Generation, IEEE Access, vol.7, pp.144618-144627, 2019. ,
A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural F0 Model for Statistical Parametric Speech Synthesis, IEEE/ACM Trans. Audio Speech Lang. Process, vol.28, pp.157-170, 2019. ,
Modulation Filter Learning Using Deep Variational Networks for Robust Speech Recognition, IEEE J. Sel. Top. Signal Process, vol.13, pp.244-253, 2019. ,
ACVAE-VC: Non-Parallel Voice Conversion With Auxiliary Classifier Variational Autoencoder, IEEE/ACM Trans. Audio Speech Lang. Process, vol.27, pp.1432-1443, 2019. ,
Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting, IEEE Trans. Sustain. Energy, vol.11, pp.571-583, 2019. ,
Collaborative Variational Deep Learning for Healthcare Recommendation, IEEE Access, vol.7, pp.55679-55688, 2019. ,
Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex, NeuroImage, vol.198, pp.125-136, 2019. ,
EEG-Based Adaptive Driver-Vehicle Interface Using Variational Autoencoder and PI-TSVM, IEEE Trans. Neural Syst. Rehabil. Eng, vol.27, pp.2025-2033, 2019. ,
VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder, Genom. Proteom. Bioinform, vol.16, pp.320-331, 2018. ,
Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators, IEEE Access, vol.8, pp.5438-5454, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02462252
Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks, Chem. Eng. Sci, vol.181, pp.68-78, 2018. ,
Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks, IEEE Geosci. Remote Sens. Lett, vol.14, pp.2395-2397, 2017. ,
Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder, IEEE Access, vol.7, pp.139086-139096, 2019. ,
Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes, IEEE Access, vol.7, pp.22554-22565, 2019. ,
A novel process monitoring approach based on variational recurrent autoencoder, Comput. Chem. Eng, vol.129, 2019. ,
Generative Adversarial Networks: An Overview, IEEE Signal Process. Mag, vol.35, pp.53-65, 2018. ,
, Deep Learning in the Biomedical Applications: Recent and Future Status. Appl. Sci, vol.9, p.1526, 2019.
An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition, Neurocomputing, vol.310, pp.213-222, 2018. ,
Partial Discharge Pattern Recognition with Data Augmentation based on Generative Adversarial Networks, Proceedings of the 2018 Condition Monitoring and Diagnosis (CMD), pp.23-26, 2018. ,
Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks, Neurocomputing, vol.315, pp.412-424, 2018. ,
Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study, IEEE Access, vol.7, pp.9515-9530, 2019. ,
Generative adversarial networks for data augmentation in machine fault diagnosis, Comput. Ind, vol.106, pp.85-93, 2019. ,
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults, Knowl. Based Syst, vol.165, pp.474-487, 2019. ,
Novel deep generative simultaneous recurrent model for efficient representation learning, Neural Netw, vol.107, pp.12-22, 2018. ,
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pp.22-29, 2017. ,
Unsupervised Dual Learning for Image-to-Image Translation, Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pp.22-29, 2017. ,
Context Encoders: Feature Learning by Inpainting, Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.27-30, 2016. ,
, Conditional Image Synthesis With Auxiliary Classifier GANs. arXiv 2016
, Semi-Supervised Learning with Generative Adversarial Networks. arXiv 2016
, Conditional Generative Adversarial Nets. arXiv, 2014.
, Least Squares Generative Adversarial Networks. arXiv 2016
, Coupled Generative Adversarial Networks. arXiv 2016
, Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv 2016
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks, 2017. ,
Image-to-Image Translation with Conditional Adversarial Networks, 2016. ,
, , 2017.
, , 2017.
, Adversarial Feature Learning. arXiv 2016
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks ,
, Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv 2016
, Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. arXiv 2016
, Adversarially Learned Inference. arXiv, 2016.
, , 2015.
Improving variational autoencoder with deep feature consistent and generative adversarial training, Neurocomputing, vol.341, pp.183-194, 2019. ,
PuVAE: A Variational Autoencoder to Purify Adversarial Examples, IEEE Access, vol.7, pp.126582-126593, 2019. ,
, A Neural Algorithm of Artistic Style. arXiv 2015
, Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv, 2014.
Understanding autoencoders with information theoretic concepts, Neural Netw, vol.117, pp.104-123, 2019. ,
How Auto-Encoders Could Provide Credit Assignment in, Deep Networks via Target Propagation. arXiv, 2014. ,
Denoising Criterion for Variational Auto-Encoding Framework, 2015. ,
Autoencoder node saliency: Selecting relevant latent representations, Pattern Recognit, vol.88, pp.643-653, 2019. ,
, Auto-Encoding Variational Bayes. arXiv 2013
Variational Inference & Deep Learning: A New Synthesis, Faculty of Science (FNWI), Informatics Institute (IVI), 2017. ,
, Variational Inference: A Review for Statisticians. arXiv 2016
, Feature Pyramid Networks for Object Detection. arXiv 2016
Intelligence artificielle: Quel avenir en anatomie pathologique?, Ann. Pathol, vol.39, pp.119-129, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02462265
Gradient-based learning applied to document recognition, Proc. IEEE, vol.86, pp.2278-2324, 1998. ,
One-shot learning by inverting a compositional causal process, Advances in Neural Information Processing Systems ,
, , pp.2526-2534, 2013.
Inductive Principles for Restricted Boltzmann Machine Learning, Machine Learning Research, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, vol.9, pp.509-516, 2010. ,
, Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv 2017
Learning Multiple Layers of Features from Tiny Images, Licensee MDPI, 2009. ,