Abdi H, Williams LJ (2010) Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2(4):433–459
Aggarwal CC, Zhai C (2012) A survey of text clustering algorithms. Mining Text Data, 77–128
Akcay S, Atapour-Abarghouei A, Breckon TP (2018) Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Computer Vision-ACCV 2018: 14th Asian conference on computer vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part III 14, Springer, pp 622–637
Alex SB, Mary L (2023) Variational autoencoder for prosody-based speaker recognition. ETRI J 45(4):678–689
Al-Qatf M, Lasheng Y, Al-Habib M, Al-Sabahi K (2018) Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access 6:52843–52856
Alsadhan N (2023) A multi-module machine learning approach to detect tax fraud. Comput Syst Sci Eng 46(1):241–253
Alzu’bi A, Albalas F, Al-Hadhrami T, Younis LB, Bashayreh A (2021) Masked face recognition using deep learning: a review. Electronics 10(21):2666
An J, Cho S (2015) Variational autoencoder based anomaly detection using reconstruction probability. Special Lecture IE 2(1):1–18
An P, Wang Z, Zhang C (2022) Ensemble unsupervised autoencoders and gaussian mixture model for cyberattack detection. Inform Process Manag 59(2):102844
Aumentado-Armstrong T, Tsogkas S, Jepson A, Dickinson S (2019) Geometric disentanglement for generative latent shape models. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 8181–8190
Azarang A, Kehtarnavaz N (2020) A review of multi-objective deep learning speech denoising methods. Speech Commun 122:1–10
Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Inst Signal Inform Process 18(1998):1–8
Bank D, Koenigstein N, Giryes R (2020) Autoencoders. arXiv preprint arXiv:2003.05991
Bank D, Koenigstein N, Giryes R (2023) Autoencoders. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook 353–374
Bank D, Koenigstein N, Giryes R (2023) Autoencoders. Machine learning for data science handbook: Data mining and knowledge discovery handbook, pp 353–374
Berahmand K, Li Y, Xu Y (2023) DAC-HPP: deep attributed clustering with high-order proximity preserve. Neural Comput Appl pp 1–19
Bertalmio M, Sapiro G, CasellesV, Ballester C (2000) Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques, pp 417–424
Bhangale KB, Kothandaraman M (2022) Survey of deep learning paradigms for speech processing. Wireless Pers Commun 125(2):1913–1949
Bursic S, Cuculo V, D’Amelio A (2019) Anomaly detection from log files using unsupervised deep learning. In: International symposium on formal methods, Springer, pp 200–207
Cacciarelli D, Kulahci M, Tyssedal J (2022) Online active learning for soft sensor development using semi-supervised autoencoders. arXiv preprint arXiv:2212.13067
Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Proceedings of the AAAI conference on artificial intelligence, vol. 30
Chai Z, Song W, Wang H, Liu F (2019) A semi-supervised auto-encoder using label and sparse regularizations for classification. Appl Soft Comput 77:205–217
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58
Charitou C, Garcez Ad, Dragicevic S (2020) Semi-supervised gans for fraud detection. In: 2020 international joint conference on neural networks (IJCNN), IEEE, pp 1–8
Charte D, Charte F, García S, del Jesus MJ, Herrera F (2018) A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines. Inform Fus 44:78–96
Che L, Yang X, Wang L (2020) Text feature extraction based on stacked variational autoencoder. Microprocess Microsyst 76:103063
Chen S, Guo W (2023) Auto-encoders in deep learning-a review with new perspectives. Mathematics 11(8):1777
Chen Y, Liu Y, Jiang D, Zhang X, Dai W, Xiong H, Tian Q (2022) Sdae: Self-distillated masked autoencoder. In: European conference on computer vision, Springer, pp 108–124
Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. arXiv preprint arXiv:1206.4683
Chowdhary K, Chowdhary K (2020) Natural language processing. Fundamentals of artificial intelligence, pp 603–649
Cui P, Wang X, Pei J, Zhu W (2018) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833–852
Daneshfar F, Soleymanbaigi S, Nafisi A, Yamini P (2023) Elastic deep autoencoder for text embedding clustering by an improved graph regularization. Expert Syst Appl 121780
Debener J, Heinke V, Kriebel J (2023) Detecting insurance fraud using supervised and unsupervised machine learning. J Risk Insurance
Dehghan A, Ortiz EG, Villegas R, Shah M (2014) Who do i look like? determining parent-offspring resemblance via gated autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1757–1764
DeLise T (2023) Deep semi-supervised anomaly detection for finding fraud in the futures market. arXiv preprint arXiv:2309.00088
Ding L, Liu G-W, Zhao B-C, Zhou Y-P, Li S, Zhang Z-D, Guo Y-T, Li A-Q, Lu Y, Yao H-W et al (2019) Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer. Chin Med J 132(04):379–387
Ding S, Keal CA, Zhao L, Yu D (2022) Dimensionality reduction and classification for hyperspectral image based on robust supervised Isomap. J Ind Prod Eng 39(1):19–29
Ding Y, Zhuang J, Ding P, Jia M (2022) Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings. Reliab Eng Syst Saf 218:108126
Dong Y, Chen K, Peng Y, Ma Z (2022) Comparative study on supervised versus semi-supervised machine learning for anomaly detection of in-vehicle can network. In: 2022 IEEE 25th international conference on intelligent transportation systems (ITSC), IEEE, pp 2914–2919
Du X, Yu J, Chu Z, Jin L, Chen J (2022) Graph autoencoder-based unsupervised outlier detection. Inf Sci 608:532–550
Dutt A, Gader P (2023) Wavelet multiresolution analysis based speech emotion recognition system using 1d CNN LSTM networks. IN: IEEE/ACM Transactions on audio, speech, and language processing
Dzakiyullah NR, Pramuntadi A, Fauziyyah AK (2021) Semi-supervised classification on credit card fraud detection using autoencoders. J Appl Data Sci 2(1):01–07
Fan H, Zhang F, Wei Y, Li Z, Zou C, Gao Y, Dai Q (2021) Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans Pattern Anal Mach Intell 44(8):4125–4138
Fanai H, Abbasimehr H (2023) A novel combined approach based on deep autoencoder and deep classifiers for credit card fraud detection. Expert Syst Appl 217:119562
Fan S, Wang X, Sh, C, Lu E, Lin K, Wang B (2020) One2multi graph autoencoder for multi-view graph clustering. In: Proceedings of the web conference 2020, pp 3070–3076
Farahnakian F, Heikkonen J (2018) A deep auto-encoder based approach for intrusion detection system. In: 2018 20th international conference on advanced communication technology (ICACT), IEEE, pp 178–183
Foti S, Koo B, Stoyanov D, Clarkson MJ (2022) 3d shape variational autoencoder latent disentanglement via mini-batch feature swapping for bodies and faces. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 18730–18739
Gaikwad SK, Gawali BW, Yannawar P (2010) A review on speech recognition technique. Int J Comput Appl 10(3):16–24
Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767
Gao Y, Wang L, Liu J, Dang J, Okada S (2023) Adversarial domain generalized transformer for cross-corpus speech emotion recognition. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2023.3290795
García-Mendoza J-L, Villaseñor-Pineda L, Orihuela-Espina F, Bustio-Martínez L (2022) An autoencoder-based representation for noise reduction in distant supervision of relation extraction. J Intell Fuzzy Syst 42(5):4523–4529
Garson GD (2022) Factor analysis and dimension reduction in R: a social Scientist’s Toolkit. Taylor & Francis, New York
Geng J, Fan J, Wang H, Ma X, Li B, Chen F (2015) High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci Remote Sens Lett 12(11):2351–2355
Ghorbani A, Fakhrahmad SM (2022) A deep learning approach to network intrusion detection using a proposed supervised sparse auto-encoder and SVM. Iran J Sci Technol Trans Electr Eng 46(3):829–846
Girin L, Leglaive S, Bie X, Diard J, Hueber T, Alameda-Pineda X (2020) Dynamical variational autoencoders: a comprehensive review. arXiv preprint arXiv:2008.12595
Gorokhov O, Petrovskiy M, Mashechkin I, Kazachuk M (2023) Fuzzy CNN autoencoder for unsupervised anomaly detection in log data. Mathematics 11(18):3995
Guo X, Liu X, Zhu E, Yin J (2017) Deep clustering with convolutional autoencoders. In: Neural information processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24, Springer, pp 373–382
Guo Z, Wang F, Yao K, Liang J, Wang Z (2022) Multi-scale variational graph autoencoder for link prediction. In: Proceedings of the Fifteenth ACM international conference on web search and data mining, pp 334–342
Guo Y, Zhou D, Ruan X, Cao J (2023) Variational gated autoencoder-based feature extraction model for inferring disease-Mirna associations based on multiview features. Neural Netw
Hadifar A, Sterckx L, Demeester T, Develder C (2019) A self-training approach for short text clustering. In: Proceedings of the 4th workshop on representation learning for NLP (RepL4NLP-2019), pp 194–199
Han C, Wang J (2021) Face image inpainting with evolutionary generators. IEEE Signal Process Lett 28:190–193
Hara K, Shiomoto K (2022) Intrusion detection system using semi-supervised learning with adversarial auto-encoder. In: NOMS 2020-2020 IEEE/IFIP network operations and management symposium, IEEE, pp 1–8
Hasan BMS, Abdulazeez AM (2021) A review of principal component analysis algorithm for dimensionality reduction. J Soft Comput Data Min 2(1):20–30
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173–182
Hickok G, Poeppel D (2007) The cortical organization of speech processing. Nat Rev Neurosci 8(5):393–402
Higgins I, Matthey L, Pal A, Burgess C, Glorot X, Botvinick M, Mohamed S, Lerchner A (2016) beta-vae: Learning basic visual concepts with a constrained variational framework. In: International conference on learning representations
Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Hoang D-T, Kang H-J (2019) A survey on deep learning based bearing fault diagnosis. Neurocomputing 335:327–335
Hoang T-N, Kim D (2022) Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders. Veh Commun 38:100520
Hosseini S, Varzaneh ZA (2022) Deep text clustering using stacked autoencoder. Multimedia tools and applications 81(8):10861–10881
Hosseini M, Celotti L, Plourde E (2021) Speaker-independent brain enhanced speech denoising. In: ICASSP 2021-2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1310–1314
Hou L, Luo X-Y, Wang Z-Y, Liang J (2020) Representation learning via a semi-supervised stacked distance autoencoder for image classification. Front Inform Technol Electron Eng 21(7):1005–1018
Hou Z, Liu X, Cen Y, Dong Y, Yang H, Wang C, Tang J (2022) Graphmae: Self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 594–604
Huang G, Jafari AH (2023) Enhanced balancing GAN: minority-class image generation. Neural Comput Appl 35(7):5145–5154
Huang Z, Jin X, Lu C, Hou Q, Cheng M-M, Fu D, Shen X, Feng J (2022) Contrastive masked autoencoders are stronger vision learners. arXiv preprint arXiv:2207.13532
Ieracitano C, Adeel A, Morabito FC, Hussain A (2020) A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing 387:51–62
Jain R, Kasturi R, Schunck BG et al (1995) Machine vision, vol 5. McGraw-hill New York, New York
Jaiswal G, Rani R, Mangotra H, Sharma A (2023) Integration of hyperspectral imaging and autoencoders: benefits, applications, hyperparameter tunning and challenges. Comput Sci Rev 50:100584
Jha S, Shah S, Ghamsani R, Sanghavi P, Shekokar NM (2023) Analysis of RNNs and different ML and DL classifiers on speech-based emotion recognition system using linear and nonlinear features. CRC Press, Boca Raton, pp 109–126
Jia K, Sun L, Gao S, Song Z, Shi BE (2015) Laplacian auto-encoders: an explicit learning of nonlinear data manifold. Neurocomputing 160:250–260
Jiang S, Dong R, Wang J, Xia M (2023) Credit card fraud detection based on unsupervised attentional anomaly detection network. Systems 11(6):305
Kennedy RK, Salekshahrezaee Z, Villanustre F, Khoshgoftaar TM (2023) Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J Big Data 10(1):106
Kim S, Jang H, Hong S, Hong YS, Bae WC, Kim S, Hwang D (2021) Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization. Med Image Anal 73:102198
Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308
Kowsari K, Jafari Meimandi K, Heidarysafa M, Mendu S, Barnes L, Brown D (2019) Text classification algorithms: a survey. Information 10(4):150
Książek K, Głomb P, Romaszewski M, Cholewa M, Grabowski B, Búza K (2022) Improving autoencoder training performance for hyperspectral unmixing with network reinitialisation. In: International Conference on Image Analysis and Processing, pp. 391–403. Springer
Kumar S, Rath SP, Pandey A (2022) Improved far-field speech recognition using joint variational autoencoder. arXiv preprint arXiv:2204.11286
Kunang YN, Nurmaini S, Stiawan D, Zarkasi A, et al (2018) Automatic features extraction using autoencoder in intrusion detection system. In: 2018 international conference on electrical engineering and computer science (ICECOS), IEEE, pp 219–224
Le T-D, Noumeir R, Rambaud J, Sans G, Jouvet P (2023) Adaptation of autoencoder for sparsity reduction from clinical notes representation learning. IEEE J Trans Eng Health Med
Lee J-w, Lee J (2017) Idae: Imputation-boosted denoising autoencoder for collaborative filtering. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp2143–2146
Lee D, Seung HS (2000) Algorithms for non-negative matrix factorization. Adv Neural Inform Process Syst 13
Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587
Lewandowski B, Paffenroth R (2022) Autoencoder feature residuals for network intrusion detection: Unsupervised pre-training for improved performance. In: 2022 21st IEEE international conference on machine learning and applications (ICMLA), IEEE, pp 1334–1341
Li Y-J, Wang S-S, Tsao Y, Su B (2021) Mimo speech compression and enhancement based on convolutional denoising autoencoder. In: 2021 Asia-pacific signal and information processing association annual summit and conference (APSIPA ASC), IEEE, pp 1245–1250
Li F, Zuraday J, Wu W (2018) Sparse representation learning of data by autoencoders with l\(\hat{}\) sub \(1/2\hat{}\) regularization. Neural Netw World 28(2):133–147
Li H, Zhang L, Huang B, Zhou X (2020) Cost-sensitive dual-bidirectional linear discriminant analysis. Inf Sci 510:283–303
Li Z, Huang H, Zhang Z, Shi G (2022) Manifold-based multi-deep belief network for feature extraction of hyperspectral image. Remote Sens 14(6):1484
Li X, Li C, Rahaman MM, Sun H, Li X, Wu J, Yao Y, Grzegorzek M (2022) A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 55(6):4809–4878. https://doi.org/10.1007/s10462-021-10121-0
Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference, pp 689–698
Liao L, Cheng G, Ruan H, Chen K, Lu J (2022) Multichannel variational autoencoder-based speech separation in designated speaker order. Symmetry 14(12):2514
Lin C-C, Hung Y, Feris R, He L (2020) Video instance segmentation tracking with a modified vae architecture. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13147–13157
Li P, Pei Y, Li J (2023) A comprehensive survey on design and application of autoencoder in deep learning. Appl Soft Comput 110176
Liu Y, Ponce C, Brunton SL, Kutz JN (2023) Multiresolution convolutional autoencoders. J Comput Phys 474:111801
Lopes IO, Zou D, Abdulqadder IH, Ruambo FA, Yuan B, Jin H (2022) Effective network intrusion detection via representation learning: a denoising autoencoder approach. Comput Commun 194:55–65
Luo W, Li J, Yang J, Xu W, Zhang J (2017) Convolutional sparse autoencoders for image classification. IEEE Trans Neural Netw Learn Syst 29(7):3289–3294
Luo W, Liu W, Gao S (2017) Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE international conference on multimedia and expo (ICME), IEEE pp 439–444
Ma M, Sun C, Chen X (2018) Deep coupling autoencoder for fault diagnosis with multimodal sensory data. IEEE Trans Ind Inf 14(3):1137–1145
Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. arXiv preprint arXiv:1511.05644
Ma S, Li X, Tang J, Guo F (2022) Eaa-net: Rethinking the autoencoder architecture with intra-class features for medical image segmentation. arXiv preprint arXiv:2208.09197
Marchi E, Vesperini F, Eyben F, Squartini S, Schuller B (2015) A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional lstm neural networks. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1996–2000. IEEE
Martínez V, Berzal F, Cubero J-C (2016) A survey of link prediction in complex networks. ACM Comput Surv 49(4):1–33
McConville R, Santos-Rodriguez R, Piechocki RJ, Craddock I (2021) N2d:(not too) deep clustering via clustering the local manifold of an autoencoded embedding. In: 2020 25th international conference on pattern recognition (ICPR), IEEE, pp 5145–5152
McKeown K (1992) Text generation. Cambridge University Press, Cambridge
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113
Medsker LR, Jain L (2001) Recurrent neural networks. Design Appl 5(64–67):2
Meyer BH, Pozo ATR, Zola WMN (2022) Global and local structure preserving GPU t-SNE methods for large-scale applications. Expert Syst Appl 201:116918
Miao J, Yang T, Sun L, Fei X, Niu L, Shi Y (2022) Graph regularized locally linear embedding for unsupervised feature selection. Pattern Recogn 122:108299
Minkin A (2021) The application of autoencoders for hyperspectral data compression. In: 2021 international conference on information technology and nanotechnology (ITNT), IEEE, pp 1–4
Miuccio L, Panno D, Riolo S (2022) A wasserstein GAN autoencoder for SCMA networks. IEEE Wireless Commun Lett 11(6):1298–1302
Molaei S, Ghorbani N, Dashtiahangar F, Peivandi M, Pourasad Y, Esmaeili M (2022) Fdcnet: presentation of the fuzzy CNN and fractal feature extraction for detection and classification of tumors. Comput Intell Neurosci 2022
Myronenko A (2019) 3d mri brain tumor segmentation using autoencoder regularization. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, Springer, pp 311–320
Ng A et al (2011) Sparse autoencoder. CS294A Lecture Notes 72(2011):1–19
Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int J Inf Manage 57:102282
Ohgushi T, Horiguchi K, Yamanaka M (2020) Road obstacle detection method based on an autoencoder with semantic segmentation. In: proceedings of the Asian conference on computer vision
Palaz D, Collobert R (2015) Analysis of CNN-based speech recognition system using raw speech as input. Report, Idiap
Palsson B, Sveinsson JR, Ulfarsson MO (2022) Blind hyperspectral unmixing using autoencoders: a critical comparison. IEEE J Sel Topics Appl Earth Observ Remote Sens 15:1340–1372
Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv 54(2):1–38
Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv 54(2):1–38
Pan S, Hu R, Long G, Jiang J, Yao L, Zhang C (2018) Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407
Pan S, Hu R, Long G, Jiang J, Yao L, Zhang C (2018) Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407
Papananias M, McLeay TE, Mahfouf M, Kadirkamanathan V (2023) A probabilistic framework for product health monitoring in multistage manufacturing using unsupervised artificial neural networks and gaussian processes. Proc Inst Mech Eng Part B: J Eng Manufact 237(9):1295–1310
Paul D, Chakdar D, Saha S, Mathew J (2023) Online research topic modeling and recommendation utilizing multiview autoencoder-based approach. IEEE Trans Comput Soc Syst
Pereira RC, Santos MS, Rodrigues PP, Abreu PH (2020) Reviewing autoencoders for missing data imputation: technical trends, applications and outcomes. J Artif Intell Res 69:1255–1285
Petersson H, Gustafsson D, Bergstrom D (2016) Hyperspectral image analysis using deep learning-a review. In: 2016 sixth international conference on image processing theory, tools and applications (IPTA), IEEE, pp 1–6
Pratella D, Ait-El-Mkadem Saadi S, Bannwarth S, Paquis-Fluckinger V, Bottini S (2021) A survey of autoencoder algorithms to pave the diagnosis of rare diseases. Int J Mol Sci 22(19):10891
Preechakul K, Chatthee N, Wizadwongsa S, Suwajanakorn S (2022) Diffusion autoencoders: Toward a meaningful and decodable representation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10619–10629
Qian J, Song Z, Yao Y, Zhu Z, Zhang X (2022) A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes. Chemometrics Intell Lab Syst, 104711
Ray P, Reddy SS, Banerjee T (2021) Various dimension reduction techniques for high dimensional data analysis: a review. Artif Intell Rev 54(5):3473–3515. https://doi.org/10.1007/s10462-020-09928-0
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: Explicit invariance during feature extraction. In: Proceedings of the 28th international conference on international conference on machine learning, pp 833–840
Rituerto-González E, Peláez-Moreno C (2021) End-to-end recurrent denoising autoencoder embeddings for speaker identification. Neural Comput Appl 33(21):14429–14439
Ruff L, Vandermeulen RA, Görnitz N, Binder A, Müller E, Müller K-R, Kloft M (2019) Deep semi-supervised anomaly detection. arXiv preprint arXiv:1906.02694
Rumelhart DE, Hinton GE, Williams RJ, et al (1985) Learning internal representations by error propagation. Institute for Cognitive Science, University of California, San Diego La
Rusnac A-L, Grigore O (2022) CNN architectures and feature extraction methods for EEG imaginary speech recognition. Sensors 22(13):4679
Sae-Ang B-I, Kumwilaisak W, Kaewtrakulpong P (2022) Semi-supervised learning for defect segmentation with autoencoder auxiliary module. Sensors 22(8):2915
Sagha H, Cummins N, Schuller B (2017) Stacked denoising autoencoders for sentiment analysis: a review. Wiley Interdiscip Rev Data Min Knowl Discov 7(5):1212
Saha S, Minku LL, Yao X, Sendhoff B, Menzel S (2022) Split-ae: An autoencoder-based disentanglement framework for 3d shape-to-shape feature transfer. In: 2022 international joint conference on neural networks (IJCNN), IEEE, pp 1–9
Sakurada M, Yairi T (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, pp. 4–11
Salehi A, Davulcu H (2019) Graph attention auto-encoders. arXiv preprint arXiv:1905.10715
Salha G, Limnios S, Hennequin R, Tran V-A, Vazirgiannis M (2019) Gravity-inspired graph autoencoders for directed link prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 589–598
Sayed HM, ElDeeb HE, Taie SA (2023) Bimodal variational autoencoder for audiovisual speech recognition. Mach Learn 112(4):1201–1226
Seki S, Kameoka H, Tanaka K, Kaneko T (2023) Jsv-vc: Jointly trained speaker verification and voice conversion models. In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1–5
Semeniuta S, Severyn A, Barth E (2017) A hybrid convolutional variational autoencoder for text generation. arXiv preprint arXiv:1702.02390
Seyfioğlu MS, Özbayoğlu AM, Gürbüz SZ (2018) Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Trans Aerosp Electron Syst 54(4):1709–1723
Shankar V, Parsana S (2022) An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing. J Acad Mark Sci 50(6):1324–1350
Shi D, Zhao C, Wang Y, Yang H, Wang G, Jiang H, Xue C, Yang S, Zhang Y (2022) Multi actor hierarchical attention critic with RNN-based feature extraction. Neurocomputing 471:79–93
Shixin P, Kai C, Tian T, Jingying C (2022) An autoencoder-based feature level fusion for speech emotion recognition. Digital Commun Netw
Shrestha N (2021) Factor analysis as a tool for survey analysis. Am J Appl Math Stat 9(1):4–11
Singh A, Ogunfunmi T (2022) An overview of variational autoencoders for source separation, finance, and bio-signal applications. Entropy 24(1):55
Smatana M, Butka P (2019) Topicae: a topic modeling autoencoder. Acta Polytechnica Hungarica 16(4):67–86
Solorio-Fernández S, Carrasco-Ochoa JA, Martínez-Trinidad JF (2022) A survey on feature selection methods for mixed data. Artif Intell Rev 55(4):2821–2846. https://doi.org/10.1007/s10462-021-10072-6
Song Y, Hyun S, Cheong Y-G (2021) Analysis of autoencoders for network intrusion detection. Sensors 21(13):4294
Song C, Liu F, Huang Y, Wang L, Tan T (2013) Auto-encoder based data clustering. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 18th Iberoamerican Congress, CIARP 2013, Havana, Cuba, November 20-23, 2013, Proceedings, Part I 18, pp 117–124. Springer
Srikotr T (2022) The improved speech spectral envelope compression based on VQ-VAE with adversarial technique. Thesis
Strub F, Mary J, Gaudel R (2016) Hybrid collaborative filtering with autoencoders. arXiv preprint arXiv:1603.00806
Strub F, Mary J, Philippe P (2015) Collaborative filtering with stacked denoising autoencoders and sparse inputs. In: NIPS workshop on machine learning for ecommerce
Su Y, Li J, Plaza A, Marinoni A, Gamba P, Chakravortty S (2019) DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Trans Geosci Remote Sens 57(7):4309–4321
Sudo T, Kanishima Y, Yanagihashi H (2021) A study of anomalous sound detection using autoencoder for quality determination and condition diagnosis. IEICE Tech. Rep. 121(284):20–25
Talpur N, Abdulkadir SJ, Alhussian H, Hasan MH, Aziz N, Bamhdi A (2023) Deep neuro-fuzzy system application trends, challenges, and future perspectives: a systematic survey. Artif Intell Rev 56(2):865–913. https://doi.org/10.1007/s10462-022-10188-3
Tanveer M, Rastogi A, Paliwal V, Ganaie M, Malik A, Del Ser J, Lin C-T (2023) Ensemble deep learning in speech signal tasks: a review. Neurocomputing 126436
Thai HH, Hieu ND, Van Tho N, Do Hoang H, Duy PT, Pham V-H (2022) Adversarial autoencoder and generative adversarial networks for semi-supervised learning intrusion detection system. In: 2022 RIVF international conference on computing and communication technologies (RIVF), IEEE, pp 584–589
Tian Y, Xu Y, Zhu Q-X, He Y-L (2022) Novel stacked input-enhanced supervised autoencoder integrated with gated recurrent unit for soft sensing. IEEE Trans Instrum Meas 71:1–9
Tian H, Zhang L, Li S, Yao M, Pan G (2023) Pyramid-VAE-GAN: transferring hierarchical latent variables for image inpainting. Comput Visual Med pp 1–15
Todd JT (2004) The visual perception of 3d shape. Trends Cogn Sci 8(3):115–121
Tripathi M (2021) Facial image denoising using autoencoder and UNET. Herit Sustain Dev 3(2):89–96
Vahdat A, Kautz J (2020) Nvae: a deep hierarchical variational autoencoder. Adv Neural Inf Process Syst 33:19667–19679
Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. Adv Neural Inform Process Syst 26
Van Der Maaten L, Postma EO, van den Herik HJ et al (2009) Dimensionality reduction: a comparative review. J Mach Learn Res 10(66–71):13
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A, Bottou L (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12)
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A, Bottou L (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(12)
Wang W, Yang D, Chen F, Pang Y, Huang S, Ge Y (2019) Clustering with orthogonal autoencoder. IEEE Access 7:62421–62432
Wang G, Karnan L, Hassan FM (2022) Face feature point detection based on nonlinear high-dimensional space. Int J Syst Assurance Eng Manag 13(Suppl 1):312–321
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1225–1234
Wang D, Li T, Deng P, Zhang F, Huang W, Zhang P, Liu J (2023) A generalized deep learning clustering algorithm based on non-negative matrix factorization. ACM Trans Knowledge Discovery Data
Wang C, Pan S, Long G, Zhu X, Jiang J (2017) Mgae: Marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 889–898
Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp1235–1244
Wu C, Wu F, Wu S, Yuan Z, Liu J, Huang Y (2019) Semi-supervised dimensional sentiment analysis with variational autoencoder. Knowl-Based Syst 165:30–39
Wubet YA, Lian K-Y (2022) Voice conversion based augmentation and a hybrid CNN-LSTM model for improving speaker-independent keyword recognition on limited datasets. IEEE Access 10:89170–89180
Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM international conference on web search and data mining, pp 153–162
Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning, PMLR, pp 478–487
Xu W, Keshmiri S, Wang G (2019) Adversarially approximated autoencoder for image generation and manipulation. IEEE Trans Multimed 21(9):2387–2396
Xu H, Ding S, Zhang X, Xiong H, Tian Q (2022) Masked autoencoders are robust data augmentors. arXiv preprint arXiv:2206.04846
Xu W, Sun H, Deng C, Tan Y (2017) Variational autoencoder for semi-supervised text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol. 31
Yan B, Han G (2018) Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system. IEEE Access 6:41238–41248
Yang B, Fu X, Sidiropoulos ND, Hong M (2017) Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: International conference on machine learning, pp 3861–3870. PMLR
Yang X, Song Z, King I, Xu Z (2022) A survey on deep semi-supervised learning. IEEE Trans Knowl Data Eng
Ye H, Zhang W, Nie M (2022) An improved semi-supervised variational autoencoder with gate mechanism for text classification. Int J Pattern Recognit Artif Intell 36(10):2253006
Ying LJ, Zainal A, Norazwan MN (2023) Stacked supervised auto-encoder with deep learning framework for nonlinear process monitoring and fault detection. In: AIP conference proceedings, vol. 2785. AIP Publishing
Yong BX, Brintrup A (2022) Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection. Expert Syst Appl 209:118196
Zabalza J, Ren J, Zheng J, Zhao H, Qing C, Yang Z, Du P, Marshall S (2016) Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 185:1–10
Zhang Y, Zhang E, Chen W (2016) Deep neural network for halftone image classification based on sparse auto-encoder. Eng Appl Artif Intell 50:245–255
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1):1–38
Zhang R, Yu L, Tian S, Lv Y (2019) Unsupervised remote sensing image segmentation based on a dual autoencoder. J Appl Remote Sens 13(3):038501–038501
Zhang G, Liu Y, Jin X (2020) A survey of autoencoder-based recommender systems. Front Comp Sci 14:430–450
Zhang G, Liu Y, Jin X (2020) A survey of autoencoder-based recommender systems. Front Comp Sci 14:430–450
Zhang S, Yao L, Xu X, Wang S, Zhu L (2017) Hybrid collaborative recommendation via semi-autoencoder. In: Neural information processing: 24th international conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part I 24, Springer, pp 185–193
Zhang C, Zhang C, Song J, Yi JSK, Kweon IS (2023) A survey on masked autoencoder for visual self-supervised learning
Zhang C, Zhang C, Song J, Yi JSK, Zhang K, Kweon IS (2022) A survey on masked autoencoder for self-supervised learning in vision and beyond. arXiv preprint arXiv:2208.00173
Zhang C, Zhang C, Song J, Yi JSK, Zhang K, Kweon IS (2022) A survey on masked autoencoder for self-supervised learning in vision and beyond. arXiv preprint arXiv:2208.00173
Zhao K, Ding H, Ye K, Cui X (2021) A transformer-based hierarchical variational autoencoder combined hidden Markov model for long text generation. Entropy 23(10):1277
Zhou F, Wang G, Zhang K, Liu S, Zhong T (2023) Semi-supervised anomaly detection via neural process. IEEE Trans Knowl Data Eng
Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3d shape retrieval. Neurocomputing 204:41–50