From neural PCA to deep unsupervised learning

ImageNet classification with deep convolutional neural networks

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Maximum Likelihood from Incomplete Data Via the EM Algorithm

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Reducing the dimensionality of data with neural networks

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Deep learning in neural networks: an overview

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A fast learning algorithm for deep belief nets

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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

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Extracting and composing robust features with denoising autoencoders

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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

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A Fast Fixed-Point Algorithm for Independent Component Analysis

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Learning in Graphical Models

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Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Simplified neuron model as a principal component analyzer

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Deep, Big, Simple Neural Nets for Handwritten Digit Recognition

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Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces

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Advances in Neural Information Processing Systems 26 (NIPS 2013)

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A Neurodynamical cortical model of visual attention and invariant object recognition

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Deep Generative Stochastic Networks Trainable by BackpropPreprint

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The nonlinear PCA learning rule in independent component analysis

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Disentangling Factors of Variation for Facial Expression Recognition

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Deep AutoRegressive NetworksPreprint

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Latent Variable Models

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Denoising Source SeparationPreprint

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Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood

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How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target PropagationPreprint

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A Deep and Tractable Density Estimator

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Hierarchical models of variance sources

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On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models

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Iterative Neural Autoregressive Distribution Estimator (NADE-k)Preprint

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Separation of Nonlinear Image Mixtures by Denoising Source Separation

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Deep Learning for IoT Big Data and Streaming Analytics: A Survey

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Semi-Supervised Learning with Ladder NetworksPreprint

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Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningPreprint

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Semi Supervised Semantic Segmentation Using Generative Adversarial Network

Souly, N., Spampinato, C., & Shah, M. A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.606 · Cited by 597

Classification-Reconstruction Learning for Open-Set Recognition

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Stacked Generative Adversarial Networks

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Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

Qi, G.-J. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01265-2 · Cited by 366

An Overview of Deep Semi-Supervised LearningPreprint

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Ladder Variational AutoencodersPreprint

Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.02282 · Cited by 346

Auxiliary Deep Generative ModelsPreprint

Maaløe, L., Sønderby, C. K., Sønderby, S. K., & Winther, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05473 · Cited by 301

Transductive Semi-Supervised Deep Learning Using Min-Max Features

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Battery Safety: Machine Learning-Based Prognostics

Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216

A semi-supervised convolutional neural network for hyperspectral image classification

Liu, B., Yu, X., Zhang, P., Tan, X., Yu, A., & Xue, Z. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1331053 · Cited by 208

Graph-based semi-supervised learning: A review

Chong, Y., Ding, Y., Yan, Q. Y., & Pan, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.12.130 · Cited by 208

Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICAPreprint

Hyvärinen, A., & Morioka, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06336 · Cited by 193

Unsupervised Representation Learning with Deep Convolutional Neural Network for Remote Sensing Images

Yu, Y., Gong, Z., Zhong, P., & Shan, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71589-6_9 · Cited by 170

Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer

Lin, S., Ji, R., Chen, C., Tao, D., & Luo, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873305 · Cited by 164

SESS: Self-Ensembling Semi-Supervised 3D Object Detection

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Deep Learning for Optimal Deployment of UAVs With Visible Light Communications

Wang, Y., Chen, M., Yang, Z., Luo, T., & Saad, W. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3007804 · Cited by 143

Semisupervised Adaptive Ladder Network for Remote Sensing Image Change Detection

Shi, J., Wu, T., Qin, A. K., Lei, Y., & Jeon, G. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3158741 · Cited by 127

Deep Clustering: On the Link Between Discriminative Models and K-Means

Jabi, M., Pedersoli, M., Mitiche, A., & Ayed, I. B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2962683 · Cited by 122

Toward an integration of deep learning and neuroscience

Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119

Semi-Supervised Speech Emotion Recognition With Ladder Networks

Parthasarathy, S., & Busso, C. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3023632 · Cited by 119

Deep Supervised Learning Using Local Errors

Mostafa, H., Ramesh, V., & Cauwenberghs, G. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00608 · Cited by 115

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image ClassificationPreprint

Zhang, Y., Lee, K., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06582 · Cited by 107

A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning

Zhu, L., Zhang, C., Zhang, C., Zhang, Z., Zhou, X., Liu, W., & Zhu, B. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.106944 · Cited by 95

Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks

Zhang, K., Tang, B., Yi, Q., & Deng, L. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.05.049 · Cited by 90

Global Versus Localized Generative Adversarial Nets

Qi, G.-J., Zhang, L., Hu, H., Edraki, M., Wang, J., & Hua, X. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00164 · Cited by 86

A Regularized Cross-Layer Ladder Network for Intrusion Detection in Industrial Internet of Things

Long, J., Liang, W., Li, K., Wei, Y., & Marino, M. D. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3204034 · Cited by 86

Deconstructing the Ladder Network ArchitecturePreprint

Pezeshki, M., Fan, L., Brakel, P., Courville, A. C., & Bengio, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06430 · Cited by 83

A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification

Kong, Y., & Ni, D. (2020) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2020.2964581 · Cited by 76

Joint optic disc and cup segmentation using semi-supervised conditional GANs

Liu, S., Hong, J., Lu, X., Jia, X., Lin, Z., Zhou, Y., Liu, Y., & Zhang, H. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103485 · Cited by 67

Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes

Lupat, R., Perera, R. D., Loi, S., & Li, J. (2023) · IEEE Access · doi:10.1109/access.2023.3240515 · Cited by 77

How to Train Deep Variational Autoencoders and Probabilistic Ladder NetworksPreprint

Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. (2016) · arXiv (Cornell University) · Cited by 69

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Kemker, R., Luu, R., & Kanan, C. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2833808 · Cited by 64

Learning Deep Representations Using Convolutional Auto-Encoders with Symmetric Skip Connections

Dong, L.-F., Gan, Y., Mao, X.-L., Yang, Y.-B., & Shen, C. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462085 · Cited by 61

Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial NetworkPreprint

Souly, N., Spampinato, C., & Shah, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09695 · Cited by 60

Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images

Krešo, I., Krapac, J., & Šegvić, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2984894 · Cited by 59

Photovoltaic Fault Diagnosis Via Semisupervised Ladder Network With String Voltage and Current Measures

Chen, S.-Q., Yang, G.-J., Gao, W., & Guo, M. (2020) · IEEE Journal of Photovoltaics · doi:10.1109/jphotov.2020.3038335 · Cited by 54

Malware Traffic Classification Using Domain Adaptation and Ladder Network for Secure Industrial Internet of Things

Ning, J., Gui, G., Wang, Y., Yang, J., Adebisi, B., Ci, S., Gacanin, H., & Adachi, F. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3131981 · Cited by 54

A Semi-Supervised Learning Approach to IEEE 802.11 Network Anomaly Detection

Jing, R., Ji, Y., & Tang, B. (2019) · IEEE Vehicular Technology Conference · doi:10.1109/vtcspring.2019.8746576 · Cited by 54

Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images

Krapac, J., & Segvic, I. K. S. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.37 · Cited by 51

Collective Decision of One-vs-Rest Networks for Open-Set Recognition

Jang, J., & Kim, C. O. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3189996 · Cited by 49

Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification

Su, H., Shi, X., Cai, J., & Yang, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_62 · Cited by 48

Deep Predictive Learning in Neocortex and Pulvinar

O’Reilly, R. C., Russin, J., Zolfaghar, M., & Rohrlich, J. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01708 · Cited by 49

Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature SegmentationPreprint

Teikari, P., Santos, M. E. C., Poon, C., & Hynynen, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02382 · Cited by 55

Deep Cross-Corpus Speech Emotion Recognition: Recent Advances and Perspectives

Zhang, S., Liu, R., Tao, X., & Zhao, X. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.784514 · Cited by 48

A review of various semi-supervised learning models with a deep learning and memory approach

Bagherzadeh, J., & Asil, H. (2018) · Iran Journal of Computer Science · doi:10.1007/s42044-018-00027-6 · Cited by 44

Learning image-based spatial transformations via convolutional neural networks: A review

Tustison, N. J., Avants, B., & Gee, J. C. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.037 · Cited by 45

Semisupervised Semantic Segmentation by Improving Prediction Confidence

Chen, H., Jin, Y., Jin, G., Zhu, C., & Chen, E. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3066850 · Cited by 43

DLIN: Deep Ladder Imputation Network

Hallaji, E., Razavi‐Far, R., & Saif, M. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3054878 · Cited by 42

RoCGAN: Robust Conditional GAN

Chrysos, G. G., Kossaifi, J., & Zafeiriou, S. P. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01348-5 · Cited by 72

MTLM: a multi-task learning model for travel time estimation

Xu, S., Zhang, R., Cheng, W., & Xu, J. (2020) · GeoInformatica · doi:10.1007/s10707-020-00422-x · Cited by 40

Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional AttributesPreprint

Parthasarathy, S., & Busso, C. (2018) · arXiv (Cornell University) · doi:10.21437/interspeech.2018-1391 · Cited by 40

Online Video Summarization: Predicting Future to Better Summarize Present

Lal, S., Duggal, S., & Indu, S. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00056 · Cited by 38

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

Baumgartner, C. F., Tezcan, K. C., Chaitanya, K., Hötker, A. M., Muehlematter, U. J., Schawkat, K., Becker, A. S., Donati, O., & Konukoglu, E. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_14 · Cited by 34

Selective Acoustic Feature Enhancement for Speech Emotion Recognition With Noisy Speech

Leem, S.-G., Fulford, D., Onnela, J., Gard, D. E., & Busso, C. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3340603 · Cited by 45

Semi-supervised Ladder Networks for Speech Emotion Recognition

Tao, J.-H., Huang, J., Li, Y., Lian, Z., & Niu, M. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1175-x · Cited by 33

Analysis of the proficiency of fully connected neural networks in the process of classifying digital images. Benchmark of different classification algorithms on high-level image features from convolutional layers

Janke, J., Castelli, M., & Popovič, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.058 · Cited by 33

Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification

Wu, S., Deng, G., Li, J., Li, R., Yu, Z., & Wong, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01033 · Cited by 37

Network Intrusion Detection Based on Semi-supervised Variational Auto-Encoder

Osada, G., Omote, K., & Nishide, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66399-9_19 · Cited by 33

Deep CNN-based Multi-task Learning for Open-Set RecognitionPreprint

Oza, P., & Patel, V. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03161 · Cited by 33

Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection

Chen, C., Dong, S., Tian, Y., Cao, K., Liu, L., & Guo, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3105807 · Cited by 35

SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion

Li, C., Wang, S., He, L., Yu, P. S., Liang, Y., & Li, Z. (2018) · Proceedings · doi:10.1109/icdm.2018.00040 · Cited by 30

Learning to Generate with MemoryPreprint

Li, C., Zhu, J., & Zhang, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07416 · Cited by 32

Developing ladder network for intelligent evaluation system: Case of remaining useful life prediction for centrifugal pumps

He, R., Dai, Y., Lu, J., & Mou, C. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.08.010 · Cited by 29

Applying deep learning on packet flows for botnet detection

van Roosmalen, J., Vranken, H., & van Eekelen, M. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167306 · Cited by 28

Generalized Loss-Sensitive Adversarial Learning with Manifold Margins

Edraki, M., & Qi, G.-J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_6 · Cited by 27

Robust Conditional Generative Adversarial NetworksPreprint

Chrysos, G. G., Kossaifi, J., & Zafeiriou, S. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08657 · Cited by 27

GAR: An efficient and scalable graph-based activity regularization for semi-supervised learning

Kilinc, O., & Uysal, I. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.028 · Cited by 26

Not All Features are Equal: Selection of Robust Features for Speech Emotion Recognition in Noisy Environments

Leem, S.-G., Fulford, D., Onnela, J.-P. “., Gard, D. E., & Busso, C. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747705 · Cited by 26

Learning ladder neural networks for semi-supervised node classification in social network

Li, B., Pi, D., & Lin, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113957 · Cited by 25

Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

Shi, J., Wu, T., Qin, A. K., Lei, Y., & Jeon, G. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3357667 · Cited by 25

Joint Learning Embeddings for Chinese Words and their Components via Ladder Structured Networks

Song, Y., Shi, S., & Li, J. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/608 · Cited by 25

A semi-supervised learning approach with consistency regularization for tumor histopathological images analysis

Jiang, Y., Sui, X., Ding, Y., Xiao, W. L., Zheng, Y., & Zhang, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.1044026 · Cited by 25

A Robust Fault Classification Method for Streaming Industrial Data Based on Wasserstein Generative Adversarial Network and Semi-Supervised Ladder Network

Zhang, C., Peng, K., Dong, J., Zhang, X., & Yang, K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3262249 · Cited by 22

Semi-supervised process fault classification based on convolutional ladder network with local and global feature fusion

Li, S., Luo, J., & Hu, Y. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106843 · Cited by 22

A Deep Learning Approach to Network Intrusion Detection Using a Proposed Supervised Sparse Auto-encoder and SVM

Ghorbani, A. A., & Fakhrahmad, S. M. (2022) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-022-00498-1 · Cited by 19

Deep ladder reconstruction-classification network for unsupervised domain adaptation

Deng, W., Su, Z., Qiu, Q., Zhao, L., Kuang, G., Pietikäinen, M., Xiao, H., & Liu, L. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.009 · Cited by 19

Deep Ladder-Suppression Network for Unsupervised Domain Adaptation

Deng, W., Zhao, L., Kuang, G., Hu, D., Pietikäinen, M., & Liu, L. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3065247 · Cited by 18

When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and DatasetsPreprint

Zhou, H.-Y., Oliver, A., Wu, J., & Zheng, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05313 · Cited by 23

Semi-Supervised Learning-Based Calibration Model Building of NIR Spectroscopy for In Situ Measurement of Biochemical Processes Under Insufficiently and Inaccurately Labeled Samples

Mu, G., Liu, T., Xue, C., & Chen, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3029456 · Cited by 17

Video Ladder NetworksPreprint

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Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning

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