Transfer Learning for Visual Categorization: A Survey

Distinctive Image Features from Scale-Invariant Keypoints

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Histograms of Oriented Gradients for Human Detection

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The Elements of Statistical Learning

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A survey on transfer learning

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A decision-theoretic generalization of on-line learning and an application to boosting

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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

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Hmdb: a large video database for human motion recognition

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Learning realistic human actions from movies

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Artificial Intelligence-Based Power-Temperature Inclusive Digital Predistortion

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Deep autoencoder based domain adaptation for transfer learning

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Deep learning and spark architecture based intelligent brain tumor MRI image severity classification

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Partial adversarial domain adaptation by dual-domain alignment for fault diagnosis of rotating machines

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Cluster Embedding Joint-Probability-Discrepancy Transfer for Cross-Subject Seizure Detection

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Informative Feature Selection for Domain Adaptation

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Domain Adaptation for Visual Understanding

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Importance-weighted conditional adversarial network for unsupervised domain adaptation

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Swallowing Pattern Classification Method Using Multichannel Surface EMG Signals of Suprahyoid and Infrahyoid Muscles

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Breast Cancer Image Multi-Classification Using Random Patch Aggregation and Depth-Wise Convolution based Deep-Net Model

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Automated quality control of vacuum insulated glazing by convolutional neural network image classification

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Lithium-Ion Battery State of Charge Estimation With Adaptability to Changing Conditions

Zhan, M., Agyeman, K. A., & Wang, X. (2023) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2023.3285405 · Cited by 23

Air pollutant prediction model based on transfer learning two-stage attention mechanism

Ma, Z., Wang, B., Luo, W., Jiang, J., Liu, D., Wei, H., & Luo, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-57784-7 · Cited by 23

Knowledge Transfer in Vision Recognition

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Technical Solution Discussion for Key Challenges of Operational Convolutional Neural Network-Based Building-Damage Assessment from Satellite Imagery: Perspective from Benchmark xBD Dataset

Su, J., Bai, Y., Wang, X., Lu, D. X., Zhao, B., Yang, H., Mas, E., & Koshimura, S. (2020) · Remote Sensing · doi:10.3390/rs12223808 · Cited by 23

Pseudo-margin-based universal domain adaptation

Yin, Y., Yang, Z., Wu, X., & Hu, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107315 · Cited by 22

Deep Transfer Learning Approach for Identifying Slope Surface Cracks

Yang, Y., & Mei, G. (2021) · Applied Sciences · doi:10.3390/app112311193 · Cited by 22

A WPT/NFC-Based Sensing Approach for Beverage Freshness Detection Using Supervised Machine Learning

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Domain Adaptation in Intelligent Ultrasonic Logging Tool: From Microseismic to Pulse-Echo

Gao, X., Shi, Y., Zhu, Q., Ли, Ж., Sun, H., Yao, Z., & Zhang, W. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3050154 · Cited by 22

Multi-objective solid waste classification and identification model based on transfer learning method

Chen, Y., Sun, J., Bi, S., Meng, C., & Guo, F. (2021) · Journal of Material Cycles and Waste Management · doi:10.1007/s10163-021-01283-8 · Cited by 23

Semantic Segmentation of Terrestrial Laser Scanning Point Clouds Using Locally Enhanced Image-Based Geometric Representations

Cai, Y., Fan, L., Atkinson, P. M., & Zhang, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3161982 · Cited by 22

A hybrid approach combining deep learning and signal processing for bearing fault diagnosis under imbalanced samples and multiple operating conditions

Zhang, B., Wang, W., & He, Y. L. (2025) · Scientific Reports · doi:10.1038/s41598-025-98138-1 · Cited by 21

Transfer Learning for Semisupervised Collaborative Recommendation

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Learning image features with fewer labels using a semi-supervised deep convolutional network

dos Santos, F. P., Zor, C., Kittler, J., & Ponti, M. A. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.016 · Cited by 21

An approach to rapid processing of camera trap images with minimal human input

Duggan, M. T., Groleau, M. F., Shealy, E. P., Self, L. S., Utter, T. E., Waller, M. M., Hall, B. C., Stone, C. G., Anderson, L., & Mousseau, T. A. (2021) · Ecology and Evolution · doi:10.1002/ece3.7970 · Cited by 21

Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

Kang, M., Lee, K. H., & Lee, Y. (2021) · Applied Sciences · doi:10.3390/app11083668 · Cited by 23

Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial

Mu, G., Zhu, Y., Niu, Z., Ding, S., Yu, H., Li, H., Wu, L., Wang, J., Luo, R., Hu, X., et al. (2021) · Endoscopy International Open · doi:10.1055/a-1372-2789 · Cited by 21

Cross-Domain Recognition via Projective Cross-Reconstruction

Fang, X., Jiang, L. C., Han, N., Sun, W., Xu, Y. D., & Xie, S. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3155145 · Cited by 21

Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition

Zhang, Y., Guo, X., Li, L., & Ansari, N. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108590 · Cited by 21

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Ragu, N., & Teo, J. (2023) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2022.1039299 · Cited by 23

大数据系统综述

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Improving classification with semi-supervised and fine-grained learning

Lai, D., Tian, W., & Chen, L. F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.12.002 · Cited by 21

Classification of Retinal Diseases Using Transfer Learning Approach

Das, A., Giri, R., Chourasia, G., & Bala, A. A. (2019) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces45898.2019.9002415 · Cited by 21

Automated identification of aquatic insects: A case study using deep learning and computer vision techniques

Simović, P. M., Milosavljević, A., Stojanović, K. Z., Radenković, M. D., Savić‐Zdravković, D., Predić, B., Petrović, A., Božanić, M. L. J., & Milošević, D. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.172877 · Cited by 20

On Learning and Learned Data Representation by Capsule Networks

Lin, A., Li, J., & Ma, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2911622 · Cited by 21

One shot ancient character recognition with siamese similarity network

Liu, X., Gao, W., Li, R., Xiong, Y., Tang, X., & Chen, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-18986-z · Cited by 22

HD2A-Net: A novel dual gated attention network using comprehensive hybrid dilated convolutions for medical image segmentation

Cui, R., Yang, R., Liu, F., & Geng, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106384 · Cited by 20

An Investigation Into the Advancements of Edge-AI Capabilities for Structural Health Monitoring

Mishra, A., Gangisetti, G., & Khazanchi, D. (2024) · IEEE Access · doi:10.1109/access.2024.3365550 · Cited by 21

An exocentric look at egocentric actions and vice versa

Ardeshir, S., & Borji, A. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.05.005 · Cited by 22

Classifying functional nuclear images with convolutional neural networks: a survey

Lin, Q., Man, Z., Cao, Y., Deng, T., Han, C., Cao, C., Zhang, L., Zeng, S., Gao, R., Wang, W., et al. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1690 · Cited by 20

Learning Transferable Sparse Representations for Cross-Corpus Facial Expression Recognition

Chen, D.-L., Song, P., & Zheng, W.-M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3077489 · Cited by 15

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Jiménez-Gaona, Y., Rodríguez-Álvarez, M. J., & Lakshminarayanan, V. (2020) · Applied Sciences · doi:10.3390/app10228298 · Cited by 21

Microwave Radiometer RFI Detection Using Deep Learning

Mohammed, P. N., & Piepmeier, J. R. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3091873 · Cited by 19

A Study on the Possible Diagnosis of Parkinson’s Disease on the Basis of Facial Image Analysis

Jakubowski, J., Potulska‐Chromik, A., Białek, K., Nojszewska, M., & Kostera‐Pruszczyk, A. (2021) · Electronics · doi:10.3390/electronics10222832 · Cited by 19

Intelligent IoT for Automotive Industry 4.0: Challenges, Opportunities, and Future Trends

Ghosh, R. K., Banerjee, A., Aich, P., Basu, D., & Ghosh, U. (2022) · Internet of things · doi:10.1007/978-3-030-81473-1_16 · Cited by 19

An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer’s disease

Song, Z., Deng, B., Wang, J., & Yi, G. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac697d · Cited by 20

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients

Ren, G., Li, B., Lam, S., Xiao, H., Huang, Y.-H., Cheung, A. L.-Y., Lu, Y., Mao, R., Ge, H., Kong, F., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.883516 · Cited by 19

Adaptive Local Discriminant Analysis and Distribution Matching for Domain Adaptation in Hyperspectral Image Classification

Ning, Y., Peng, J., Sun, L., Huang, Y., Sun, W., & Du, Q. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3181577 · Cited by 19

Truly Generalizable Radiograph Segmentation With Conditional Domain Adaptation

Oliveira, H., Ferreira, E., & Santos, J. A. D. (2020) · IEEE Access · doi:10.1109/access.2020.2991688 · Cited by 19

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Dias, M. A., da Silva, E. A., de Azevedo, S. C., Casaca, W. C. D. O., Statella, T., & Negri, R. G. (2019) · Remote Sensing · doi:10.3390/rs12010043 · Cited by 19

Image Retrieval by Fusion of Features from Pre-trained Deep Convolution Neural Networks

Bhandi, V., & Devi, K. A. S. (2019) · International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) · doi:10.1109/icatiece45860.2019.9063814 · Cited by 19

Learn from one data set to classify all – A multi-target domain adaptation approach for white blood cell classification

Baydi̇lli̇, Y. Y., Atila, Ü., & Elen, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105645 · Cited by 19

Learning to Learn Sequential Network Attacks Using Hidden Markov Models

Chadza, T., Kyriakopoulos, K. G., & Lambotharan, S. (2020) · IEEE Access · doi:10.1109/access.2020.3011293 · Cited by 19

A transductive transfer learning approach for image classification

Rezaei, S., Tahmoresnezhad, J., & Solouk, V. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01200-9 · Cited by 19

Deep learning approach to classify Tiger beetles of Sri Lanka

Abeywardhana, L., DANGALLE, C. D., Nugaliyadde, A., & Mallawarachchi, Y. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101286 · Cited by 19

A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)

Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18

Automation of image categorization with most relevant negatives

Kumar, V. D. A., & Kumar, V. D. A. (2017) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661817030051 · Cited by 18

Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments

Alkurdi, A., He, M., Cerna, J., Clore, J. L., Sowers, R. B., Hsiao‐Wecksler, E. T., & Hernandez, M. E. (2025) · Sensors · doi:10.3390/s25041241 · Cited by 19

AO-UOD: A Novel Paradigm for Underwater Object Detection Using Acousto–Optic Fusion

Yu, F., Xiao, F., Li, C., Cheng, E., & Yuan, F. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3529121 · Cited by 20

Deep learning for locally advanced nasopharyngeal carcinoma prognostication based on pre- and post-treatment MRI

Li, S., Deng, Y., Hua, H., Li, S., Chen, X., Xie, B., Zhu, Z., Liu, R., Huang, J., & Tao, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106785 · Cited by 18

A Deep Neural Network Sensor for Visual Servoing in 3D Spaces

Durdevic, P., & Ortíz-Arroyo, D. (2020) · Sensors · doi:10.3390/s20051437 · Cited by 18

From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays

Oliveira, H., Mota, V. F., Machado, A. M. C., & dos Santos, J. A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.09.021 · Cited by 18

Perlin Random Erasing for Data Augmentation

Saran, M., Nar, F., & Saran, A. N. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477804 · Cited by 18

Transfer Learning Techniques

Weiss, K. R., Khoshgoftaar, T. M., & Wang, D. (2016) · Springer eBooks · doi:10.1007/978-3-319-44550-2_3 · Cited by 18

Recognizing military vehicles in social media images using deep learning

Hiippala, T. (2017) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2017.8004875 · Cited by 22

Transfer Learning in Earth Observation Data Analysis: A review

Nowakowski, A., Rosso, M. P. D., Zachar, P., Spiller, D., Gabara, G., Barretta, D., Kalinowska, K. B., Choromański, K., Wilkowski, A., Sebastianelli, A., et al. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3494673 · Cited by 17

An automatic and integrated self-diagnosing system for the silting disease of drainage pipelines based on SSAE-TSNE and MS-LSTM

Di, D., Wang, D., Fang, H., He, Q., Zhou, L., Chen, X., Sun, B., & Zhang, J. (2023) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2023.105076 · Cited by 17

MCCFNet: Multi-channel Color Fusion Network For Cognitive Classification of Traditional Chinese Paintings

Geng, J., Zhang, X., Yan, Y., Sun, M., Zhang, H., Assaad, M. M., Ren, J., & Li, X. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10172-1 · Cited by 17

Image-based preliminary emergency assessment of damaged buildings after earthquake: Taiwan case studies

Cheng, M., Khasani, R. R., & Citra, R. J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107164 · Cited by 18

Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning

Pan, G., Wu, Q., Zhou, B., Li, J., Wang, W., Ding, G., & Yau, D. K. Y. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3495812 · Cited by 17

Shipwrecks Detection Based on Deep Generation Network and Transfer Learning with Small Amount of Sonar Images

Xu, L., Wang, X., & Wang, X. (2019) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls.2019.8909011 · Cited by 17

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification

Sima, H., Wang, J., Guo, P., Sun, J., Liu, H., Xu, M., & Zou, Y. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3080304 · Cited by 17

SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation

Liu, Y., Yang, G., Huang, Y., & Yin, Y. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-210597 · Cited by 17

Beyond Supervised Learning: A Computer Vision Perspective

Chum, L., Subramanian, A., Balasubramanian, V. N., & Jawahar, C. V. (2019) · Journal of the Indian Institute of Science · doi:10.1007/s41745-019-0099-3 · Cited by 18

A Hybrid CNN-LSTM Architecture for High Accurate Edge-Assisted Bandwidth Prediction

Wen, H., Yu, J., Pan, G., Chen, X., Zhang, S., & Xu, S. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3213017 · Cited by 16

Arabic Captioning for Images of Clothing Using Deep Learning

Al-Malki, R. S., & Al-Aama, A. Y. (2023) · Sensors · doi:10.3390/s23083783 · Cited by 16

Multiple structured latent double dictionary pair learning for cross-domain industrial process monitoring

Deng, Z., Chen, X., Xie, Y., Zou, Z., & Zhang, H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119514 · Cited by 16

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Zhao, Q., Lan, Y., Yin, X., & Wang, K. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01172-6 · Cited by 17

A Deep Learning Method for Dynamic Process Modeling of Real Landslides Based on Fourier Neural Operator

Chen, Y., Ouyang, C., Xu, Q., & Yang, W. (2024) · Earth and Space Science · doi:10.1029/2023ea003417 · Cited by 16

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review

Rousta, F., Esteki, A., Shalbaf, A., Sadeghi, A., Moghadam, P. K., & Voshagh, A. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108205 · Cited by 16

Transfer metric learning for unsupervised domain adaptation

Huang, J., & Zhou, Z. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5871 · Cited by 16

A decadal survey of zero-shot image classification

Pang, Y., Wang, H., Yu, Y., & Ji, Z. (2019) · Scientia Sinica Informationis · doi:10.1360/n112018-00312 · Cited by 16

A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionPreprint

Chen, R., Guo, Y., Zheng, H., & Jiang, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01800 · Cited by 18

Pretrained transformer framework on pediatric claims data for population specific tasks

Zeng, X., Linwood, S. L., & Liu, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-07545-1 · Cited by 15

Digit Recognition in Sign Language Based on Convolutional Neural Network and Support Vector Machine

Alom, M. S., Hasan, M. J., & Wahid, M. F. (2019) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti47673.2019.9067999 · Cited by 15

Development and validation of a composite AI model for the diagnosis of levator ani muscle avulsion

Wu, S., Ren, Y., Lin, X., Huang, Z., Zheng, Z., & Zhang, X. (2022) · European Radiology · doi:10.1007/s00330-022-08754-y · Cited by 15

Augmented bilinear network for incremental multi-stock time-series classification

Shabani, M., Tran, D. T., Kanniainen, J., & Iosifidis, A. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109604 · Cited by 15

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

Yang, J., & Xiang, Y. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3277076 · Cited by 15

Discriminative and domain invariant subspace alignment for visual tasks

Rezaei, S., & Tahmoresnezhad, J. (2019) · Iran Journal of Computer Science · doi:10.1007/s42044-019-00037-y · Cited by 15

Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial optimization

Martinez, A. D., Osaba, E., Ser, J. D., & Herrera, F. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185667 · Cited by 15

DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm

Beck, K. S., Gil, B. M., Na, S. J., Hong, J. H., Chun, S. H., An, H. J., Kim, J. J., Hong, S. A., Lee, B., Shim, W. S., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.661244 · Cited by 15

Coarse-to-Fine Joint Distribution Alignment for Cross-Domain Hyperspectral Image Classification

Miao, J., Zhang, B., & Wang, B. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3129177 · Cited by 15

A Survey of Deep Learning Models for Medical Image Analysis

Umer, M., Sharma, S., & Rattan, P. (2021) · International Conference on Computing Sciences (ICCS) · doi:10.1109/iccs54944.2021.00021 · Cited by 15

Object Detection using Transfer Learning for Underwater Robot

Wang, C.-C., & Samani, H. A. (2020) · International Conference on Advanced Robotics and Intelligent Systems (ARIS) · doi:10.1109/aris50834.2020.9205774 · Cited by 15

An Integrated Sparse Gated Graph Density Network Based on Transfer Learning for Multi-Site Probabilistic Forecasting of Renewable Energy

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A recent survey of heterogeneous transfer learning

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