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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A Novel Malware Traffic Classification Method using Semi-Supervised Learning

Ning, J., Wang, Y., Yang, J., Gacanin, H., & Ci, S. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-fall52928.2021.9625438 · Cited by 8

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

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MERLIN-Seg: Self-supervised despeckling for label-efficient semantic segmentation

Dalsasso, E., Rambour, C., Trouvé, N., & Thome, N. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103940 · Cited by 8

Semi-supervised learning for detection of sedges in sod farms

Chen, X., Liu, T., Han, K., Jin, X., & Yu, J. (2024) · Crop Protection · doi:10.1016/j.cropro.2024.106626 · Cited by 8

Balanced data driven sparsity for unsupervised deep feature learning in remote sensing images classification

Yu, Y., Zhong, P., & Gong, Z. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127041 · Cited by 7

Teaching Deep Learners to Generalize

Aggarwal, C. C. (2018) · Springer eBooks · doi:10.1007/978-3-319-94463-0_4 · Cited by 7

Modified Semi-Supervised Adversarial Deep Network and Classifier Combination for Segmentation of Satellite Images

Barthakur, M., Sarma, K. K., & Mastorakis, N. E. (2020) · IEEE Access · doi:10.1109/access.2020.3005085 · Cited by 7

Rolling bearing fault diagnosis based on probabilistic mixture model and semi-supervised ladder network

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A Survey on the Semi Supervised Learning Paradigm in the Context of Speech Emotion Recognition

Andrade, G., Rodrigues, M., & Nováis, P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82196-8_57 · Cited by 7

Robust Semantic Segmentation with Ladder-DenseNet ModelsPreprint

Krešo, I., Oršić, M., Bevandić, P., & Šegvić, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03465 · Cited by 7

An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification

Nagesh, A., & Surdeanu, M. (2018) · International Conference on Computational Linguistics · Cited by 6

Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift

Nagesh, A., & Surdeanu, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2057 · Cited by 6

A new approach to image classification based on a deep multiclass AdaBoosting ensemble

Asil, H., & Bagherzadeh, J. (2020) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v10i5.pp4872-4880 · Cited by 6

Local Clustering with Mean Teacher for Semi-supervised learning

Chen, Z., Dutton, B., Ramachandra, B., Wu, T., & Vatsavai, R. R. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412469 · Cited by 6

A Semisupervised Approach for Language Identification based on Ladder Networks

Ben-Reuven, E., & Goldberger, J. (2016) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2016-46 · Cited by 6

Modeling Hebb Learning Rule for Unsupervised Learning

Liu, J., Gong, M., & Miao, Q. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/322 · Cited by 5

Learning to sense from events via semantic variational autoencoder

Gôlo, M. P. S., Rossi, R. G., & Marcacini, R. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260701 · Cited by 5

Semi-supervised recommender system for bone implant ratio recommendation

Qiu, X., Tan, X., Yan, F., Su, Q., Chen, J., & Jiang, X. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03156-2 · Cited by 5

Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural NetworksPreprint

Kilinc, O., & Uysal, I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08648 · Cited by 5

Advance Security and Challenges with Intelligent IoT Devices

Sharma, N., & Panwar, D. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-15-6707-0_17 · Cited by 4

On Deep Representation Learning from Noisy Web ImagesPreprint

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Virtual Adversarial Ladder Networks For Semi-supervised LearningPreprint

Shinoda, S., Worrall, D. E., & Brostow, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07476 · Cited by 5

Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsPreprint

Dong, J., Mao, X.-J., Shen, C., & Yang, Y.-B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09119 · Cited by 4

Osmosis: Generative AI and XR for the real-time transformation of urban architectural environments

Paterakis, I., & Manoudaki, N. (2025) · International Journal of Architectural Computing · doi:10.1177/14780771251356526 · Cited by 4

Sparse Autoencoder Based Semi-Supervised Learning for Phone Classification with Limited Annotations

Dhaka, A. K., & Salvi, G. (2017) · doi:10.21437/glu.2017-5 · Cited by 3

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro, S. A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131024 · Cited by 5

Dimensionality Reduction for Low-Latency High-Throughput Fraud Detection on Datastreams

Axenie, C., Tudoran, R., Bortoli, S., Hassan, M. A. H., Sánchez, C. S., & Brasche, G. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00194 · Cited by 3

Image Segmentation of Meliponine Bee using Mask- RCNN

Nizam, A., Mohd-Isa, W.-N., & Ali, A. (2020) · International Journal of Engineering Trends and Technology · doi:10.14445/22315381/cati2p203 · Cited by 3

Semi-Supervised Learning for Aspect-Based Sentiment Analysis

Zheng, H., Zhang, J., Suzuki, Y., Fukumoto, F., & Nishizaki, H. (2021) · International Conference on Cyberworlds (CW) · doi:10.1109/cw52790.2021.00042 · Cited by 3

Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders

Prokhorov, V., Li, Y., Shareghi, E., & Collier, N. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.5 · Cited by 6

Regularized error-in-variable estimation for big data modeling and process analytics

Krüger, U., Wang, X., Embrechts, M. J., Almansoori, A. S., & Hahn, J. (2022) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.105060 · Cited by 3

Ladder Networks: Learning under Massive Label Deficit

Mirza, B., Syed, T., Memon, J., & Malik, Y. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080769 · Cited by 3

Proposing a new method of image classification based on the AdaBoost deep belief network hybrid method

Asil, H., & Bagherzadeh, J. (2019) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v17i5.11797 · Cited by 3

A Distributed Semi-Supervised Platform for DNase-Seq Data Analytics using Deep Generative Convolutional Networks

Shams, S., Platania, R., Kim, J., Zhang, J., Lee, K., Yang, S., & Park, S. (2018) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3233547.3233601 · Cited by 2

Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study

Li, N., Huang, H., Lv, L., Lu, H. J., & Liu, X. (2021) · International Urology and Nephrology · doi:10.1007/s11255-020-02771-w · Cited by 2

Semantic contrast with uncertainty-aware pseudo label for lumbar semi-supervised classification

Hai, J., Chen, J., Qiao, K., Liang, N., Su, Z., Lv, H., & Yan, B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108754 · Cited by 2

Semi-supervised learning-based virtual adversarial training on graph for molecular property prediction

Lu, Y., Wang, C., Wang, Z., Zhang, X., Zhou, G., & Li, C. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.110 · Cited by 3

Demystifying Variational Diffusion Models

Ribeiro, F. D. S., & Glocker, B. (2025) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000113 · Cited by 2

Radio individual identification based on semi-supervised rectangular network

Lei, Y. (2019) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1117/12.2516756 · Cited by 2

Deep Learning-Based Super-Resolution Reconstruction and Algorithm Acceleration of Mars Hyperspectral CRISM Data

Sun, M., & Chen, S. (2022) · Remote Sensing · doi:10.3390/rs14133062 · Cited by 2

Teaching Deep Learners to Generalize

Aggarwal, C. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-29642-0_5 · Cited by 2

NARPCA: Neural Accumulate-Retract PCA for Low-Latency High-Throughput Processing on Datastreams

Axenie, C., Tudoran, R., Bortoli, S., Hassan, M. A. H., & Brasche, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30487-4_20 · Cited by 1

Learning from partially labeled data.

Mehrkanoon, S., Huang, X., & Suykens, J. A. K. (2020) · Research Publications (Maastricht University) · Cited by 2

A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean

Li, L., Long, S., Bi, J., Wang, G., Zhang, J., & Tao, X. (2021) · Web Intelligence · doi:10.3233/web-210476 · Cited by 2

EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing ImageryPreprint

Kemker, R., Gewali, U. B., & Kanan, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00292 · Cited by 2

Improving Embedding Extraction for Speaker Verification with Ladder NetworkPreprint

Tao, F., & Tür, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09125 · Cited by 2

Consistency Regularization with Generative Adversarial Networks for Semi-Supervised LearningPreprint

Chen, Z., Ramachandra, B., & Vatsavai, R. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03844 · Cited by 3

On the Importance of Encrypting Deep Features

Ni, X., Huttunen, H. J., & Rahtu, E. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00460 · Cited by 2

WorP: A Novel Worker Performance Prediction Model for General Tasks on Crowdsourcing Platforms

Xing, Q., Zhao, W., Yang, J. Y., Wu, J. X., & Wang, Q. (2021) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws53863.2021.00032 · Cited by 1

Ensemble learning of daboosting based on deep weighting for classification of hand-written numbers in Persian

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Research on SLAM System incorporating Weakly-Supervised Learning in Dynamic Environment

Cheng, Y., & Fang, R. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164673 · Cited by 1

A Survey of Artificial Intelligence Techniques for User Perceptions’ Extraction from Social Media Data

Shaikh, S., Yayilgan, S. Y., Zoto, E., & Abomhara, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-10464-0_43 · Cited by 1

A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

TAKEUCHI, M., IMAFUKU, T., Sakai, Y., & Goto, M. (2023) · Total Quality Science · doi:10.17929/tqs.9.109 · Cited by 1

Feature extraction and face recognition algorithm

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SPICE: Streaming PCA Fault Identification and Classification Engine in Predictive Maintenance

Axenie, C., Tudoran, R., Bortoli, S., Hassan, M. A. H., Wieder, A., & Brasche, G. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43887-6_27 · Cited by 1

Equivariance and Invariance for Robust Unsupervised and Semi-Supervised Learning

Zhang, L. (2020) · Journal of International Crisis and Risk Communication Research · Cited by 1

meanNet: A Multi-layer Label Mean Based Semi-supervised Neural Network Approach for Credit Prediction

Wang, G., Li, L., & Zhang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60259-8_48 · Cited by 1

Unsupervised learning for a clustering algorithm based on ellipsoidal calculus

Guarneros-Sandoval, A., Salgado, I., & Chaírez, I. (2020) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit49905.2020.9263838 · Cited by 1

PB-Worker: A Novel Participating Behavior-based Worker Ability Model for General Tasks on Crowdsourcing Platforms

Xing, Q., Zhao, W., Yang, J. Y., Wu, J. X., & Wang, Q. (2020) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws49710.2020.00012 · Cited by 2

Consistency Regularization with Generative Adversarial Networks for Semi-Supervised Image Classification.Preprint

Chen, Z., Ramachandra, B., & Vatsavai, R. R. (2020) · arXiv (Cornell University) · Cited by 1

Adversarial Ladder NetworksPreprint

Molano, J. M., Colomer, A. A., & Palacios, R. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02320

Deep Learning Methods For Visual Object Recognition

Hailat, Z. (2018) · Human Biology

Exploring the Role of Semi-Supervised Deep Reinforcement Learning and Ensemble Methods in Support of the Internet of Things

Mohammadi, M. (2018)

A Study on Large-scale Deep Learning in Bioinformatics and Biomedical Applications

Shams, S. (2019) · doi:10.31390/gradschool_dissertations.4965

Learning and representing attributed graphs

Hu, R. (2019) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney)

Improved Training for Self-Training by Confidence AssessmentsPreprint

Hyams, G., Greenfeld, D., & Bank, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.00209

Adapting the variational auto encoder for datasets with large amounts of missing values.

Fasel, R. (2019)

Data Management and Mining Technologies to Manage and Analyze Data in IoT

Sharma, S., Sasubilli, S. M., & Bhargava, K. (2020) · Wiley eBooks · doi:10.1002/9781119778868.ch10

Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT

Chauhan, K., & Dutt, V. (2020) · Wiley eBooks · doi:10.1002/9781119778868.ch11

Pap smear images classification based on surrounding tissues: A comparative study

Alsalatie, M., Mustafa, W. A., Nammneh, L., Almashakbah, F., Alquran, H., & Yacob, Y. M. (2024) · AIP conference proceedings · doi:10.1063/5.0180674

Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification

Hai, J., Chen, J., Qiao, K., Su, Z., Lü, H., & Yan, B. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01167-x

Process fault classification based on label propagation and fault relevant ladder network

Guo, X., Li, D., & Li, Y. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adfa69

Semi-supervised dictionary learning and Semi-supervised deep neural network

Tran, K.-H. (2021) · theses.fr (ABES) · doi:10.70675/42000ac0z65ebz4a00z9dd3z6af0c02d8611

Meaningful audio synthesis and musical interactions by representation learning of sound sample databases

Bitton, A. (2021) · theses.fr (ABES) · doi:10.70675/8c6172a2z19c4z4a60z9bffz9af76d821b8a

Apprentissage profond faiblement supervisé et semi-supervisé pour la détection d'évènements sonores

Cances, L. (2021) · theses.fr (ABES) · doi:10.70675/693c7da2z0b9bz4b7czaf67zee6b3ee93379

Network Architecture Search-Based Remote Sensing Image Change Detection

Shi, J. D., Lei, Y., Gong, M., & Zhang, N. (2026) · SpringerBriefs in computer science · doi:10.1007/978-981-92-1404-4_5

Deep Neural Networks-Based Heterogeneous Remote Sensing Image Change Detection

Shi, J. D., Lei, Y., Gong, M., & Zhang, N. (2026) · SpringerBriefs in computer science · doi:10.1007/978-981-92-1404-4_4

Deep Predictive Learning: A Comprehensive Model of Three Visual Streams

O'Reilly, R. C., Wyatte, D. R., & Rohrlich, J. (2017) · arXiv · doi:10.48550/arxiv.1709.04654 · Cited by 26

Lateral Connections in Denoising Autoencoders Support Supervised LearningPreprint

Rasmus, A., Harri Valpola, B., & Raiko, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08215 · Cited by 14

Learning Deep Autoencoders without Layer-wise Training.Preprint

Zhou, Y. (2014) · arXiv (Cornell University) · Cited by 1

Denoising auto-encoders toward robust unsupervised feature representation

Xiong, W., Du, B., Zhang, L., Zhang, L., & Tao, D. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727820 · Cited by 5

Unsupervised Learning with Neural Networks: Autoencoders

McClarren, R. G. (2021) · Springer eBooks · doi:10.1007/978-3-030-70388-2_8

Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Du, B., Xiong, W., Wu, J. X., Zhang, L., Zhang, L., & Tao, D. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2536638 · Cited by 414