Improving Deep Learning with Generic Data Augmentation

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

Untitled work

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971

Untitled work

(2010) · Cited by 8,261

Two-Stream Convolutional Networks for Action Recognition in Videos

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models

Rikhari, H., Kayal, E. B., Ganguly, S., Sasi, A., Sharma, S., Antony, A., Rangarajan, K., Bakhshi, S., Kandasamy, D., & Mehndiratta, A. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03222-y · Cited by 20

An improved age invariant face recognition using data augmentation

Okokpujie, K., John, S. N., Ndujiuba, C. U., Badejo, J. A., & Noma‐Osaghae, E. (2020) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v10i1.2356 · Cited by 20

An Empirical Survey of Data Augmentation for Limited Data Learning in NLPPreprint

Chen, J., Tam, D., Raffel, C., Bansal, M., & Yang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07499 · Cited by 20

Phage‐Based Biocontrol Strategies and Application in Aquatic Animal Disease Prevention and Control

Yang, L., Zhong, W., Tang, T., He, M., Zhang, T., Zhou, B., Yin, Y., Guo, J., & Gao, Z. (2025) · Reviews in Aquaculture · doi:10.1111/raq.70024 · Cited by 19

Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

Wang, R., Chen, F., Chen, H., Lin, C. X., Shuai, J., Wu, Y., Ma, L., Hu, X., Wu, M. H., Wang, J., et al. (2025) · MedComm · doi:10.1002/mco2.70247 · Cited by 22

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Burrows, H., Zarrin, J., Saheer, L. B., & Oghaz, M. M. (2021) · Electronics · doi:10.3390/electronics11010118 · Cited by 19

Deep neural networks based predictive-generative framework with data augmentation for designing composite materials

Ashank, Chakravarty, S., Garg, P., Kumar, A., Agnihotri, P. K., & Agrawal, M. K. (2022) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/ac88e8 · Cited by 23

aiWATERS: an artificial intelligence framework for the water sector

Vekaria, D., & Sinha, S. (2024) · AI in Civil Engineering · doi:10.1007/s43503-024-00025-7 · Cited by 28

Mitigating Algorithmic Bias: Evolving an Augmentation Policy that is Non-Biasing

Smith, P., & Ricanek, K. (2020) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw50321.2020.9096905 · Cited by 19

Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning

Osahor, U., & Nasrabadi, N. M. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00210 · Cited by 20

Power Insulator Defect Detection Method Based on Enhanced YOLOV7 for Aerial Inspection

Hu, J. S., Wan, W., Qiao, P., Zhou, Y., & Ouyang, A. (2025) · Electronics · doi:10.3390/electronics14030408 · Cited by 18

Estimation of Body Mass Index from photographs using deep Convolutional Neural Networks

Pantanowitz, A., Cohen, E., Gradidge, P. J.-L., Crowther, N. J., Aharonson, V., Rosman, B., & Rubin, D. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100727 · Cited by 21

Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks

Reder, S., Mund, J., Albert, N., Waßermann, L., & Miranda, L. (2021) · Remote Sensing · doi:10.3390/rs14010075 · Cited by 18

Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient

Chen, L., Ji, P., & Ma, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3210347 · Cited by 24

An Analysis of Engineering Students’ Responses to an AI Ethics Scenario

Orchard, A., & Radke, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26880 · Cited by 18

LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites

Rizvi, S. H. M., Abbas, M., Zaidi, S. S. H., Tayyab, S. M., & Malik, A. (2024) · Applied Sciences · doi:10.3390/app14072925 · Cited by 21

Phenotyping Alfalfa (Medicago sativa L.) Root Structure Architecture via Integrating Confident Machine Learning with ResNet-18

Weihs, B. J., Tang, Z., Tian, Z., Heuschele, D. J., Siddique, A., Terrill, T. H., Zhang, Z., York, L. M., Zhang, Z., & Xu, Z. (2024) · Plant Phenomics · doi:10.34133/plantphenomics.0251 · Cited by 18

A microstructure sensitive machine learning-based approach for predicting fatigue life of additively manufactured parts

Kishore, P., Mondal, A., Trivedi, A., Singh, P. K., & Alankar, A. (2024) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2024.108724 · Cited by 17

Physics-guided graph convolutional network for damage severity and zone identification in industrial composites

Azad, M. M., Jung, J., Kim, H. S., Munyaneza, O., & Sohn, J. W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103701 · Cited by 17

Efficient improvement of classification accuracy via selective test-time augmentation

Son, J., & Kang, S. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119148 · Cited by 18

VEPL Dataset: A Vegetation Encroachment in Power Line Corridors Dataset for Semantic Segmentation of Drone Aerial Orthomosaics

Cano-Solis, M., Ballesteros, J. R., & Branch, J. W. (2023) · Data · doi:10.3390/data8080128 · Cited by 19

Improving YOLOX network for multi-scale fire detection

Wang, T., Wang, J., Wang, C., Lei, Y., Cao, R., & Wang, L. (2023) · The Visual Computer · doi:10.1007/s00371-023-03178-1 · Cited by 21

An innovative method integrating two deep learning networks and hyperparameter optimization for identifying fiber optic temperature measurements in earth-rock dams

Xu, L., Wen, Z., Su, H., Cola, S., Fabbian, N., Feng, Y., & Yang, S. (2024) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2024.103802 · Cited by 17

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin

Kircher, M., Chludzinski, E., Krepel, J., Saremi, B., Beineke, A., & Jung, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052481 · Cited by 17

Improving PET-CT Image Segmentation via Deep Multi-modality Data Augmentation

Cao, K., Bi, L., Feng, D. D., & Kim, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61598-7_14 · Cited by 17

Deep Learning Architectures for Navigation Using Forward Looking Sonar Images

Almanza-Medina, J. E., Henson, B. T., & Zakharov, Y. V. (2021) · IEEE Access · doi:10.1109/access.2021.3061440 · Cited by 18

General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data AugmentationPreprint

Nanni, L., Brahnam, S., Ghidoni, S., & Maguolo, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08084 · Cited by 20

Augmenting correlation structures in spatial data using deep generative modelsPreprint

Klemmer, K., Koshiyama, A., & Flennerhag, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09796 · Cited by 17

D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection

Hirahara, K., Nakane, C., Ebisawa, H., Kuroda, T., Iwaki, Y., Utsumi, T., Nomura, Y., Koike, M., & Mineno, H. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109849 · Cited by 17

tanh As a robust feature scaling method in training deep learning models with imbalanced data

Yang, A., Zhang, M., Chen, H., Li, T., Liu, S., & Xu, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111746 · Cited by 16

Intelligent Maintenance Approaches for Improving Photovoltaic System Performance and Reliability

Marangis, D., Tziolis, G., Livera, A., Makrides, G., Kyprianou, A., & Georghiou, G. E. (2025) · Solar RRL · doi:10.1002/solr.202500289 · Cited by 16

Enhanced Data Augmentation using GANs for Raman Spectra Classification

Frischia, S. D., Giammatteo, P., Angelini, F., Spizzichino, V., Santis, E. D., & Pomante, L. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377977 · Cited by 16

Classification of melanonychia, Beau’s lines, and nail clubbing based on nail images and transfer learning techniques

Soğukkuyu, D. Y. C., & Ata, O. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1533 · Cited by 19

Achieving Generalizable Robustness of Deep Neural Networks by Stability Training

Laermann, J., Samek, W., & Strodthoff, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33676-9_25 · Cited by 19

Deep learning for object detection in fine-art paintings

Smirnov, S., & Eguizabal, A. (2018) · IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage (MetroArchaeo) · doi:10.1109/metroarchaeo43810.2018.9089828 · Cited by 18

Speaker Recognition with ResNet and VGG Networks

Jakubec, M., Lieskovská, E., & Jarina, R. (2021) · International Conference Radioelektronika · doi:10.1109/radioelektronika52220.2021.9420202 · Cited by 18

Towards Rotation Invariance in Object Detection

Kalra, A., Stoppi, G., Brown, B., Agarwal, R., & Kadambi, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00351 · Cited by 17

Digital twin-based non-destructive testing method for ultimate load-carrying capacity prediction

Huang, L., Xu, Z., Gao, T., Liu, X., Bi, Q., Wang, B., & Tian, K. (2024) · Thin-Walled Structures · doi:10.1016/j.tws.2024.112223 · Cited by 15

On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing

Okimura, I., Reid, M., Kawano, M., & Matsuo, Y. (2022) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2022.insights-1.12 · Cited by 16

Research On Maize Disease Identification Methods In Complex Environments Based On Cascade Networks And Two-Stage Transfer Learning

Liu, H., Lv, H., Li, J., Liu, Y., & Deng, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-23484-3 · Cited by 16

Hierarchical temporal prediction captures motion processing along the visual pathway

Singer, Y., Taylor, L., Willmore, B. D. B., King, A. J., & Harper, N. S. (2023) · eLife · doi:10.7554/elife.52599 · Cited by 25

MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

Müller, D., & Krämer, F. (2019) · Kidney Tumor Segmentation Challenge (KiTS) · doi:10.24926/548719.074 · Cited by 15

Fast Measurement With Chemical Sensors Based on Sliding Window Sampling and Mixed-Feature Extraction

Zhang, Q., Li, S., Tang, W., & Guo, X. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2985034 · Cited by 17

Application of ANN for Fault Detection in Overhead Transport Systems for Semiconductor Fab

Zhakov, A., Zhu, H., Siegel, A., Rank, S., Schmidt, T., Fienhold, L., & Hummel, S. (2020) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2020.2984326 · Cited by 15

Rapid detection of soil heavy metal pollution using hyperspectral data and multiscale spatial network

Wang, H., Xiao, S., Shen, R., Cheng, Q., & Zhang, S. (2025) · Environmental Technology & Innovation · doi:10.1016/j.eti.2025.104031 · Cited by 14

Detecting the Simultaneous Occurrence of Strawberry Fungal Leaf Diseases with a Deep Normalized CNN

Kerre, D., & Muchiri, H. (2022) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3529399.3529424 · Cited by 14

Leaf-Counting in Monocot Plants Using Deep Regression Models

Xie, X., Ge, Y., Walia, H., Yang, J., & Yu, H. (2023) · Sensors · doi:10.3390/s23041890 · Cited by 18

Coating Defect Detection Method Based on Data Augmentation and Network Optimization Design

Tang, K. W., Zi, B., Xu, F., Zhu, W. D., & Feng, K. L. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3277979 · Cited by 14

Image-to-Image Translation-Based Structural Damage Data Augmentation for Infrastructure Inspection Using Unmanned Aerial Vehicle

Gwon, G.-H., Lee, J.-H., Kim, I., Baek, S.-C., & Jung, H. (2023) · Drones · doi:10.3390/drones7110666 · Cited by 14

Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour segmentation

Quinton, F., Presles, B., Leclerc, S., Nodari, G., Lopez, O., Chevallier, O., Pellegrinelli, J., Vrigneaud, J., Popoff, R., Mériaudeau, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-53528-9 · Cited by 14

mmDetect: YOLO-Based Processing of mm-Wave Radar Data for Detecting Moving People

Raimondi, M., Ciattaglia, G., Nocera, A., Senigagliesi, L., Spinsante, S., & Gambi, E. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3366588 · Cited by 15

Optimization of Deep Learning Models to Predict Lung Cancer Using Chest X-Ray Images

Alzaidi, E. R. (2024) · International Academic Journal of Science and Engineering · doi:10.71086/iajse/v11i1/iajse1140 · Cited by 27

Deep learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms

Matsulevits, A., Pierrick Coupé, A., Nguyen, H., Talozzi, L., Foulon, C., Nachev, P., Corbetta, M., Tourdias, T., & de Schotten, M. T. (2024) · Brain Communications · doi:10.1093/braincomms/fcae338 · Cited by 14

Video Recognition of American Sign Language Using Two-Stream Convolution Neural Networks

Nugraha, F., & Djamal, E. C. (2019) · doi:10.1109/iceei47359.2019.8988872 · Cited by 14

Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion

Wang, R., Guo, J., Zhou, Z., Wang, K., Gou, S., Xu, R., Sher, D. J., & Wang, J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac72f0 · Cited by 14

Data Augmentation On-the-fly and Active Learning in Data Stream Classification

Malialis, K., Papatheodoulou, D., Filippou, S., Panayiotou, C. G., & Polycarpou, M. M. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022133 · Cited by 16

Prostate Ultrasound Image Segmentation Based on DSU-Net

Wang, X., Chang, Z., Zhang, Q., Li, C., Miao, F., & Gao, G. (2023) · Biomedicines · doi:10.3390/biomedicines11030646 · Cited by 14

Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modellingPreprint

Arbabyazd, L., Shen, K., Wang, Z., Hofmann-Apitius, M., Ritter, P., Initiative, T. A. D. N., McIntosh, A. R., Battaglia, D. A., & Jirsa, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.18.911248 · Cited by 14

Text Data Augmentation Techniques for Fake News Detection in the Romanian Language

Bucos, M., & Țucudean, G. (2023) · Applied Sciences · doi:10.3390/app13137389 · Cited by 11

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Rathore, A., Sharma, A., Shah, S., Sharma, N., Torney, C. J., & Guttal, V. (2023) · PeerJ · doi:10.7717/peerj.15573 · Cited by 14

LocMix: local saliency-based data augmentation for image classification

Yan, L., Ye, Y., Wang, C., & Sun, Y. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02852-0 · Cited by 13

SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)

Xu, Z., Liu, T., Xia, Z., Fan, Y., Yan, M., & Dang, X. (2024) · Sensors · doi:10.3390/s24196237 · Cited by 14

Three-dimensional cohesive finite element simulations coupled with machine learning to predict mechanical properties of polymer-bonded explosives

Lu, D., Zhang, B., Liu, L., Zhang, H., Cao, L., & Zhou, Y. (2024) · Composites Science and Technology · doi:10.1016/j.compscitech.2024.110947 · Cited by 13

Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment

Zahra, E., Mosalpuri, M., Baltrušaitis, J., Dubey, P., & Wright, M. M. (2025) · Sustainability Science and Technology · doi:10.1088/2977-3504/ada77b · Cited by 13

Research trends in livestock facial identification: a review

Kang, M.-H., & Oh, S.-H. (2025) · Journal of Animal Science and Technology · doi:10.5187/jast.2025.e4 · Cited by 15

Semantic segmentation for crack detection via generative knowledge distillation

Shim, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106201 · Cited by 13

Delaunay Triangulation Data Augmentation Guided by Visual Analytics for Deep Learning

Peixinho, A. Z., Benato, B. C., Nonato, L. G., & Falcão, A. X. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00056 · Cited by 13

Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks

Castellano, G., Castiello, C., Mencar, C., & Vessio, G. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206974 · Cited by 14

COVID-19 Electrocardiograms Classification using CNN Models

Shahin, I. M. A., Nassif, A. B., & Alsabek, M. B. (2021) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese54285.2021.9719358 · Cited by 12

Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

Kumar, V., Maheshwary, R., & Pudi, V. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.310 · Cited by 14

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

Zhang, L., Lei, Z., Xiao, C., Du, Z., Jiang, C., Yuan, X., Hu, Q., Zhai, S., Xu, L., Liu, C., et al. (2022) · Frontiers in Sensors · doi:10.3389/fsens.2022.1015223 · Cited by 13

Assessment of deep learning-based image analysis for disaster waste identification

Zhang, Y., Kim, Y., & Cha, G.-W. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.139351 · Cited by 14

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects

Liu, M., Srivastava, G. N., Ramanujam, J., & Bryliński, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-51940-9 · Cited by 13

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox

Yue, Y., Li, Z., Zhang, X., Wang, Z., Zhang, X., Zhang, F., Saravanakumar, S., Xu, J., Zhao, W., Liu, J., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109766 · Cited by 14

Interactive system based on artificial intelligence and robotic arm to enhance arabic sign language learning in deaf children

Nedjar, I., & M’hamedi, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12826-5 · Cited by 15

Mask-guided dual-perception generative adversarial network for synthesizing complex maize diseased leaves to augment datasets

Zhang, Z., Zhan, W., Sun, Y., Peng, J., Zhang, Y., Guo, Y., Sun, K., & Gui, L. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108875 · Cited by 13

Internal quality prediction technology for ‘Sulhyang’ strawberry fruit using organic analysis and hyperspectral imaging

Lee, S.-D., Gil, C. S., Lee, J.-H., Jeong, H. B., Kim, J., Jang, Y.-A., Kim, D.-Y., Lee, W.-M., & Moon, J.-H. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.124912 · Cited by 12

Blind super-resolution network based on local fuzzy discriminative loss for fabric data augmentation

Dai, N., Hu, X., Xu, K., Hu, X., Yuan, Y., Cao, B., & Shi, L. (2025) · Journal of Engineered Fibers and Fabrics · doi:10.1177/15589250241313158 · Cited by 12

Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection

Price, S. E., Gleason, M. A., Sousa, B. C., Cote, D. L., & Neamtu, R. (2021) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-021-00240-5 · Cited by 12

Nucleotide augmentation for machine learning-guided protein engineering

Minot, M., & Reddy, S. T. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac094 · Cited by 13

ADA: Adversarial Data Augmentation for Object Detection

Behpour, S., Kitani, K., & Ziebart, B. D. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00137 · Cited by 12

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases

Wang, Y., Li, F., Bharathwaj, M., Rosas, N. C., Leier, A., Akutsu, T., Webb, G. I., Marquez‐Lago, T. T., Li, J., Lithgow, T. J., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa301 · Cited by 12

Convolutional neural network model for synchrotron radiation imaging datasets to automatically detect interfacial microstructure: An in situ process monitoring tool during solar PV ribbon fabrication

Kunwar, A., Malla, P. B., Sun, J., Qu, L., & Ma, H. (2021) · Solar Energy · doi:10.1016/j.solener.2021.06.006 · Cited by 12

SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and ResolutionPreprint

Billot, B., Greve, D. N., Puonti, O., Thielscher, A., Leemput, K. V., Fischl, B. R., Dalca, A. V., & Iglesias, J. E. (2021) · arXiv (Cornell University) · Cited by 12

Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture

Derraz, R., Muharam, F. M., Jaafar, N. M., & Yap, N. K. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107646 · Cited by 11

RFIDeep: Unfolding the potential of deep learning for radio‐frequency identification

Bardon, G., Cristofari, R., Winterl, A., Barracho, T., Benoiste, M., Ceresa, C., Chatelain, N., Courtecuisse, J., Fernandes, F. A. N., Gauthier‐Clerc, M., et al. (2023) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.14187 · Cited by 11

Investigating Effective Geometric Transformation for Image Augmentation to Improve Static Hand Gestures with a Pre-Trained Convolutional Neural Network

Awaluddin, B.-A., Chao, C., & Chiou, J.-S. (2023) · Mathematics · doi:10.3390/math11234783 · Cited by 12

Diagnostic System Based on Deep Learning to Detect Diabetic Retinopathy

Singh, D., Dobhal, D. C., & Pant, J. (2024) · Pakistan Journal of Ophthalmology · doi:10.36351/pjo.v40i3.1771 · Cited by 11

Fully Automatic and Precisely Woven Fabric Defect Detection Using Improved YOLOv7-Tiny Model Utilizing Enhanced Residual Convolutional Network

Barman, J., & Kuo, C. J. (2024) · Fibers and Polymers · doi:10.1007/s12221-024-00811-1 · Cited by 11

GMSRI: A Texture-Based Martian Surface Rock Image Dataset

Wang, C., Zhang, Z., Zhang, Y., Tian, R., & Ding, M. (2021) · Sensors · doi:10.3390/s21165410 · Cited by 11

The Lean Data Scientist

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Improving Deep Learning using Generic Data AugmentationPreprint

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Data Augmentation For Deep Learning Using Generative Adversarial Networks

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Effect of Data Augmentation on the Accuracy of Convolutional Neural Networks

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