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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An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound Image Slices

Pintelas, E. G., Livieris, I. E., Barotsis, N., Panayiotakis, G., & Pintelas, P. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-79150-6_17 · Cited by 14

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

Xie, R., Wei, H., Feng, L., & An, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20851 · Cited by 14

Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Sulemane, S., Matos-Carvalho, J. P., Pedro, D., Moutinho, F., & Correia, S. D. (2022) · Algorithms · doi:10.3390/a15120440 · Cited by 14

Discriminative Subspace Alignment for Unsupervised Visual Domain Adaptation

Sun, H., Liu, S., & Zhou, S. (2016) · Neural Processing Letters · doi:10.1007/s11063-015-9494-6 · Cited by 14

Active Image Synthesis for Efficient Labeling

Chen, J., Xie, Y., Wang, K., Zhang, C., Vannan, M. A., Wang, B., & Qian, Z. Y. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2993221 · Cited by 14

Vision-based Object Classification using Deep Learning for Inventory Tracking in Automated Warehouse Environment

Patel, A. D., & Chowdhury, A. R. (2020) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas50221.2020.9268394 · Cited by 14

Near Infrared Spectroscopy (NIRS) Model Based on Transfer Learning

Han, C., Cui, J., Fu, Y., Zhang, M., Zhang, L., Kong, J., Ji, H., & Guo, C. (2024) · Analytical Letters · doi:10.1080/00032719.2024.2417349 · Cited by 13

AdapINT: A Flexible and Adaptive In-Band Network Telemetry System Based on Deep Reinforcement Learning

Zhang, P., Zhang, H., Pi, Y., Cao, Z., Wang, J., & Liao, J. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3427403 · Cited by 15

Convolutional Block Attention Module-based Deep Learning Model for MRI Brain Tumor Identification (ResNet-CBAM)

Shyamala, N., & MahaboobBasha, S. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722246 · Cited by 13

Adaptively Transfer Category-Classifier for Handwritten Chinese Character Recognition

Zhu, Y., Zhuang, F., Yang, J., Yang, X. J., & He, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16148-4_9 · Cited by 13

Object Detection for P&ID Images using various Deep Learning Techniques

Gada, M. (2021) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci50826.2021.9402386 · Cited by 13

Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis

Li, Y., Liu, Z., Yao, L., Monaghan, J. J. M., & McAlpine, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3225089 · Cited by 14

Semantic Correlation Transfer for Heterogeneous Domain Adaptation

Zhao, Y., Li, S., Zhang, R., Liu, C. H., Cao, W., Wang, X., & Tian, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3199619 · Cited by 14

Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures

Kumar, P., Purohit, G., Tanwar, P. K., & Kota, S. R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15136-z · Cited by 13

Visual Domain Adaptation through Locality Information

Devika, A. K., Sanodiya, R. K., Jose, B. R., & Mathew, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106172 · Cited by 13

A holistic multi-source transfer learning approach using wearable sensors for personalized daily activity recognition

Jia, Q., Guo, J. M., Yang, P., & Yang, Y. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01218-w · Cited by 13

A Secure Mobile Authentication Alternative to Biometrics

Azimpourkivi, M., Topkara, U., & Cărbunar, B. (2017) · Annual Computer Security Applications Conference · doi:10.1145/3134600.3134619 · Cited by 13

Face Recognition using Transferred Deep Learning for Feature Extraction

Phornchaicharoen, A., & Padungweang, P. (2019) · Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) · doi:10.1109/ecti-ncon.2019.8692306 · Cited by 13

A Comparative Study of Transfer Learning Approaches for Video Anomaly Detection

Gutoski, M., Ribeiro, M., Hattori, L. T., Romero, M., Lazzaretti, A. E., & Lopes, H. S. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421520030 · Cited by 13

The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks

Bai, L., Chen, S., Gao, M., Abdelrahman, L., Alghamdi, M., & Abdel-Mottaleb, M. S. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629607 · Cited by 13

Color object recognition via cross-domain learning on RGB-D images

Huang, Y., Zhu, F., Shao, L., & Frangi, A. F. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487308 · Cited by 12

Minimax entropy-based co-training for fault diagnosis of blast furnace

Gao, D., Yang, C., Yang, B., Chen, Y., & Deng, R. (2023) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2022.12.007 · Cited by 12

Learning from small data: Classifying sex from retinal images via deep learning

Berk, A., Özturan, G., Delavari, P., Maberley, D., Yılmaz, Ö., & Oruç, İ. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289211 · Cited by 12

Multiple Human Activities Classification Based on Dynamic On-Body Propagation Characteristics Using Transfer Learning

Zhang, Y., Shao, Y., Luo, R., Xiong, L. Y., & Zhang, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3321056 · Cited by 12

Revisiting pretraining for semi-supervised learning in the low-label regime

Xu, X., Liao, J., Cai, L., Nguyen, M. C., Lu, K., Zhang, W., Yazıcı, Y., & Foo, C.-S. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126971 · Cited by 12

A comprehensive review of datasets for detection and localization of video anomalies: a step towards data-centric artificial intelligence-based video anomaly detection

Nayak, R., Pati, U. C., & Das, S. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17889-z · Cited by 12

Contextual feature representation for image-based insider threat classification

Duan, S.-M., Yuan, J.-T., & Wang, B. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103779 · Cited by 12

Mutual Supervision of MFL Heterogeneous Signals for Insufficient Sample Defect Detection on Pipeline Safety Operation

Jiang, L., Zhang, H., Liu, J., Shen, X., & Xu, H. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3369659 · Cited by 12

A novel spectrum enhancement technique for multi-temporal, multi-spectral data using spatial-temporal filtering

Albanwan, H., & Qin, R. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.05.020 · Cited by 12

A Method for Power System Transient Stability Assessment Based on Transfer Learning

Ren, J., Chen, J., Li, B., Zhao, M., Shi, H., & You, H. (2020) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm41954.2020.9282073 · Cited by 12

MetaTP

Zhong, W., Suo, Q., Gupta, A., Jia, X., Qiao, C., & Su, L. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478083 · Cited by 12

Using Transfer Learning and Pattern Recognition to Implement a Smart Waste Management System

Aggarwal, V., Gupta, V., Gupta, S., Sharma, N., Sharma, K., & Sharma, N. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532732 · Cited by 12

Benchmarking Domain Adaptation Methods on Aerial Datasets

Nagananda, N., Taufique, A. M. N., Madappa, R., Jahan, C. S., Minnehan, B., Rovito, T. V., & Savakis, A. E. (2021) · Sensors · doi:10.3390/s21238070 · Cited by 12

Attention Regularized Laplace Graph for Domain Adaptation

Luo, L., Chen, L., & Hu, S. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3216781 · Cited by 12

A Twin Learning Framework for Traveling Salesman Problem Based on Autoencoder, Graph Filter, and Transfer Learning

Wu, J., Yang, H., Zeng, Y., Wu, Z., Liu, J., & Feng, L. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3269071 · Cited by 12

Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection

Zhu, C., Liang, J., & Zhou, F. (2023) · Information · doi:10.3390/info14100560 · Cited by 12

Enhancing Diabetic Retinopathy Detection Using Pixel Color Amplification and EfficientNetV2: A Novel Approach for Early Disease Identification

Kao, Y.-H., & Lin, C. (2024) · Electronics · doi:10.3390/electronics13112070 · Cited by 12

Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning

Sun, S., Liu, H., Meng, J., Chen, C. L. P., & Yang, Y. F. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2638321 · Cited by 12

Transfer learning for PLDA-based speaker verification

Hong, Q., Li, L., Zhang, J., Wan, L., & Guo, H. (2017) · Speech Communication · doi:10.1016/j.specom.2017.05.004 · Cited by 12

Breach detection and mitigation of UAVs using deep neural network

Shijith, N., Poornachandran, P., Sujadevi, V. G., & Dharmana, M. M. (2017) · International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) · doi:10.1109/rdcape.2017.8358297 · Cited by 12

Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images

Hashemi, S., Veisi, H., Jafarzadehpur, E., Rahmani, R., & Heshmati, Z. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02154-4 · Cited by 12

Dual-graph regularized discriminative transfer sparse coding for facial expression recognition

Chen, D., & Song, P. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102906 · Cited by 12

Vision based Human Activity Recognition using Deep Transfer Learning and Support Vector Machine

Verma, K. K., & Singh, B. M. (2021) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon52273.2021.9667661 · Cited by 13

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Yu, N., Qian, L., Huang, Y., & Wu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2928843 · Cited by 12

Scalable GP with hyperparameters sharing based on transfer learning for solving expensive optimization problems

Hu, C., Zeng, S., & Li, C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110866 · Cited by 11

Unsupervised Multi-task Learning with Hierarchical Data Structure

Cao, W., Qian, S., Wu, S., & Wong, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.08.021 · Cited by 11

Incremental Unsupervised Domain-Adversarial Training of Neural Networks

Gallego, A., Calvo-Zaragoza, J., & Fisher, R. B. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3025954 · Cited by 14

Landslide Classification from Synthetic Aperture Radar Images Using Convolutional Neural Network with Multichannel Information

Mabu, S., Nakayama, Y., & Kuremoto, T. (2020) · Journal of Signal Processing · doi:10.2299/jsp.24.61 · Cited by 11

Design and Development of Granular Fuzzy Rule-Based Models for Knowledge Transfer

Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3184716 · Cited by 11

MRISNet:Deep-learning-based Martian instance segmentation against blur

Liu, M., Liu, J., & Ma, X. (2022) · Earth Science Informatics · doi:10.1007/s12145-022-00890-9 · Cited by 11

Learn to Adapt for Self-Supervised Monocular Depth Estimation

Sun, Q., Yen, G. G., Tang, Y. S., & Zhao, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3289051 · Cited by 13

LMDT: A weakly-supervised large-margin-domain-transfer for handwritten digit recognition

Hosseinzadeh, H., & Razzazi, F. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.02.014 · Cited by 11

A domain-transfer support vector machine for multi-temporal remote sensing imagery classification

Guo, Y., Jia, X., & Paull, D. J. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127428 · Cited by 11

Evaluation of Deep Neural Network and Alternating Decision Tree for Kiwifruit Detection

Kuang, Y. C., Streeter, L. V., Cree, M. J., & Ooi, M. P. (2019) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2019.8826818 · Cited by 11

Enhancing unsupervised domain adaptation by discriminative relevance regularization

Zhang, W., Zhang, X., Lan, L., & Luo, Z. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01466-z · Cited by 11

Compensation of Measurement Errors for a Magnetoresistive Angular Sensor Array Using Artificial Neuronal Networks

Meier, P., Rohrmann, K., Sandner, M., & Prochaska, M. (2020) · IEEE Access · doi:10.1109/access.2020.3012064 · Cited by 13

Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique

Yu, B., Tola, K. D., Lee, C., & Park, S. (2021) · Sensors · doi:10.3390/s21217105 · Cited by 12

Practice makes perfect: An adaptive active learning framework for image classification

Ye, Z., Liu, P., Liu, J., Tang, X., & Zhao, W. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.091 · Cited by 11

Facial Expression Based Imagination Index and a Transfer Learning Approach to Detect Deception

Hasan, K., Rahman, W., Gerstner, L., Sen, T., Lee, S., Haut, K. G., & Hoque, M. E. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925473 · Cited by 11

The successes and pitfalls: Deep‐learning effectiveness in a Chernobyl field camera trap application

Maile, R. E., Duggan, M. T., & Mousseau, T. A. (2023) · Ecology and Evolution · doi:10.1002/ece3.10454 · Cited by 10

Harmonic Interference Prediction of Power Amplifiers by Artificial Neural Network Behavioral Model

Liu, P., Liu, D., Li, Y., Zhang, Z., Cai, S., & Su, D. (2024) · IEEE Transactions on Electromagnetic Compatibility · doi:10.1109/temc.2024.3403752 · Cited by 10

Machine Tool Fault Diagnosis Model Based on Discrete Wavelet Transform and Transfer Learning

Kuo, P., Liu, T., Hsu, P.-W., Lin, Y.-S., & Yau, H. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3405965 · Cited by 10

An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification

Souza, V. L. F., Oliveira, A. L. I., Cruz, R. M. O., & Sabourin, R. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413073 · Cited by 10

Post-training Iterative Hierarchical Data Augmentation for Deep Networks

Khan, A., & Fraz, K. (2020) · Repository@Hull (Worktribe) (University of Hull) · Cited by 10

Discrepancy-Guided Domain-Adaptive Data Augmentation

Gao, J., Hua, Y., Hu, G., Wang, C., & Robertson, N. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3128401 · Cited by 11

Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets

Sun, W., Gao, H., Chen, Y., Wang, Z., Duan, L., Tan, S., & Fang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105800 · Cited by 10

Few-Shot SAR Target Classification Combining Both Spatial and Frequency Information

Li, H., Wang, T., & Wang, S. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001168 · Cited by 10

Low-rank constraint-based multiple projections learning for cross-domain classification

Guo, W., Fang, X., Jiang, L. C., Han, N., & Teng, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110734 · Cited by 10

Learning Discriminatively Reconstructed Source Data for Object Recognition With Few Examples

Hsiao, P.-H., Chang, F.-J., & Lin, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2572602 · Cited by 10

A subspace learning-based method for JPEG mismatched steganalysis

Xue, Y., Yang, L., Wen, J., Niu, S., & Zhong, P. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6719-5 · Cited by 10

NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING

Mosalam, K. M., Muin, S., & Gao, Y. (2019) · NED University Journal of Research · doi:10.35453/nedjr-stmech-2019-0006 · Cited by 11

Conditional Adversarial Transfer for Glaucoma Diagnosis

Wang, J., Yan, Y., Xu, Y., Zhao, W., Min, H., Tan, M., & Liu, J. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857308 · Cited by 10

To Share or Not to Share? Performance Guarantees and the Asymmetric Nature of Cross-Robot Experience Transfer

Sorocky, M. J., Zhou, S., & Schoellig, A. P. (2020) · IEEE Control Systems Letters · doi:10.1109/lcsys.2020.3005886 · Cited by 10

Zero-shot Learning via the fusion of generation and embedding for image recognition

Zhao, P., Zhang, S., Liu, J., & Liu, H. (2021) · Information Sciences · doi:10.1016/j.ins.2021.08.061 · Cited by 10

A Deep Learning-Based Feature Extraction Model for Classification Brain Tumor

Jain, A., Pandey, M., & Sahu, S. K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6289-8_42 · Cited by 10

Review on deep learning-based plant disease detection

Bhise, D. V., Kumar, S., & Mohapatra, H. (2022) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca55336.2022.10009290 · Cited by 10

Transfer Deep Learning for Dielectric Profile Reconstruction in Microwave Medical Imaging

Xue, F., Guo, L., Bialkowski, A., & Abbosh, A. M. (2024) · IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · doi:10.1109/jerm.2024.3402048 · Cited by 9

Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation

Li, L., Lu, T., Sun, Y., Gao, Y., Yan, C. C., Hu, Z., & Huang, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3431283 · Cited by 9

Forecasting masked‐load with invisible distributed energy resources based on transfer learning and Bayesian tuning

Zhou, Z., Ren, C., & Xu, Y. H. (2024) · Energy Conversion and Economics · doi:10.1049/enc2.12130 · Cited by 9

A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction

Daoud, S. S., Nasayreh, A. N., Nahar, K. M. O., Abedalaziz, W., Alayasreh, S. M., Gharaibeh, H., Bashkami, A. A., Jaradat, A. A., Jarrar, S. M., Al-Hawamdeh, H., et al. (2025) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2025.100193 · Cited by 9

Transfer Learning by Sample Selection Bias Correction and Its Application in Communication Specific Emitter Identification

Liu, Z., Yang, J., Liu, H., & Wang, W. (2016) · Journal of Communications · doi:10.12720/jcm.11.4.417-427 · Cited by 9

Learning-Based Image Synthesis for Hazardous Object Detection in X-Ray Security Applications

Kim, H., Cho, S.-J., Baek, S.-J., Jung, S., & Ko, S.-J. (2021) · IEEE Access · doi:10.1109/access.2021.3116255 · Cited by 9

Image Data Augmentation techniques for Deep Learning -A Mirror Review

Saini, D., & Malik, R. (2021) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito51393.2021.9596262 · Cited by 9

Deep neural network based fruit identification and grading system for precision agriculture

Mohapatra, D., Das, N., & Mohanty, K. K. (2022) · Proceedings of the Indian National Science Academy · doi:10.1007/s43538-022-00079-0 · Cited by 9

Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests

Ranger, J., Schmidt, N., & Wolgast, A. (2022) · Educational and Psychological Measurement · doi:10.1177/00131644221132723 · Cited by 9

Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images

Wu, M., Yan, C., & Sen, G. (2025) · Scientific Reports · doi:10.1038/s41598-024-85004-9 · Cited by 9

Distribution-preserving data augmentation

Saran, N., Saran, M., & Nar, F. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.571 · Cited by 9

Deep learning for wireless modulation classification based on discrete wavelet transform

Al‐Makhlasawy, R. M., Ghanem, H. S., Kasem, H. M., Elsabrouty, M. M., Hamed, H. F. A., El‐Samie, F. E. A., & Salama, G. M. (2021) · International Journal of Communication Systems · doi:10.1002/dac.4980 · Cited by 9

Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

Cholachgudda, K. E., Biradar, R. C., Akansie, K. Y. O., Lohith, R., & Purushotham, A. A. R. (2021) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc53172.2021.9641733 · Cited by 9

An Improved Document Image Classification using Deep Transfer Learning and Feature Reduction

(2021) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2021/141022021 · Cited by 9

Integrating unsupervised and reinforcement learning in human categorical perception: A computational model

Granato, G., Cartoni, E., Rold, F. D., Mattera, A., & Baldassarre, G. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267838 · Cited by 9

Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies

Ye, Y., Pan, T., Meng, Q., Li, J., & Shen, H. T. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3177769 · Cited by 9

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Liu, X., Xing, F., You, J., Lu, J., Kuo, C. ‐. J., Fakhri, G. E., & Woo, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3192315 · Cited by 9

Moving cast shadow detection using scale-relation multi-layer pooling features

Lin, C.-W. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.06.028 · Cited by 9

Classification of Ecological Data by Deep Learning

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Classification and Region Analysis of COVID-19 Infection Using Lung CT Images and Deep Convolutional Neural NetworksPreprint

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