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

(2010) · Cited by 14,134

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

Deep Learning-Based Cloud Detection in High-Resolution Satellite Imagery Using Various Open-Source Cloud Images

Yun, Y., Kim, T., Lee, C., & Han, Y. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10281948 · Cited by 1

Performance Analysis of DCT Based Latent Space Image Data Augmentation Technique

Suryawanshi, V., & Sarode, T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-4071-4_18 · Cited by 1

Challenges for Predictive Quality in Multi-stage Manufacturing: Insights from Literature Review

Cassoli, B. B., & Metternich, J. (2023) · doi:10.1145/3617573.3618030 · Cited by 1

Enhancing Object Detection in YouTube Thumbnails Forensics with YOLOv4

Sayyafzadeh, S., Chi, H., Ho, S. M., & Mkpong-Ruffin, I. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386427 · Cited by 1

Automatic Detection of Ascaris Lumbricoides in Microscopic Images using Convolutional Neural Networks (CNN)

Pastor, G. G. M., Ruelas, C. R. A., Castro-Gutiérrez, E., & Huerta, V. L. V. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150590 · Cited by 1

Failure Detection on 3D Printing Images with Color-based Data Augmentation

Edlim, F. W., Mafazy, M. M., Ansori, D. B., Klemm, S. F., & Purwitasari, D. (2024) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml61815.2024.10578178 · Cited by 1

Mapping Planted Forests in the Korean Peninsula Using Artificial Intelligence

Mitra, A., Álvarez, C. I., Abbasi, A. O., Harris, N. L., Shao, G., Pijanowski, B. C., Jahanshahi, M. R., Gamarra, J. G. P., Kim, H. S., Kim, T., et al. (2024) · Forests · doi:10.3390/f15071216 · Cited by 1

Enhancing Medical Imaging Through Data Augmentation: A Review

Teixeira, B., Pinto, G., Filipe, V. M. D. J., & Teixeira, A. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65223-3_23 · Cited by 1

Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL

Demeusy, V., Roche, F., Vincent, F., Taha, M., Zhang, R., Jouvent, É., Chabriat, H., & Lebenberg, J. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108936 · Cited by 1

Multiple Instance Pathology Image Diagnosis Model based on Channel Attention and Data Augmentation

Wan, T., Tian, J., Wei, P., & Li, J. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06093-9 · Cited by 1

Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release

Henriksen, A. A., & Mundt, M. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671468 · Cited by 1

Generative Adversarial Network Optimization Algorithm Based on Adaptive Data Augmentation

Yu, Y., Shi, D., & Pan, Q. (2024) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026824500214 · Cited by 1

Using Data Augmentation to Support AI-Based Requirements Evaluation in Large-Scale Projects

Korfmann, R., Beyersdorffer, P., Münch, J., & Kuhrmann, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-71139-8_7 · Cited by 1

Assessment of Deep Learning Based Image Segmentation for Identifying Floating Net Cages from Very High-Resolution Capella Synthetic Aperture Radar (SAR) Data

Arjasakusuma, S., & Kusuma, S. S. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-02022-w · Cited by 1

Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Maß, V., Seidl-Schulz, J., Leipnitz, M., Fritzsche, E., Geyer, M., Pflanz, M., & Reim, S. (2024) · Agronomy · doi:10.3390/agronomy14112643 · Cited by 1

Augmentation Techniques for Balancing Spatial Datasets in Machine and Deep Learning Applications

Belussi, A., Garofolo, D., & Migliorini, S. (2024) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3678717.3691230 · Cited by 1

Enhancing Electron Microscopy Image Classification Using Data Augmentation

Welsman, J. A., Weber, G. H., Amusat, O., Giannakou, A., & Ramakrishnan, L. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00016 · Cited by 1

Enhancing Classification Models With Sophisticated Counterfactual Images

Li, X., Togo, R., Maeda, K., Ogawa, T., & Haseyama, M. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3530843 · Cited by 1

Enhancing NetVLAD Based localization with Gaussian Splat

Fodor, K. F., & Rövid, A. (2025) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami63904.2025.10883320 · Cited by 1

Research on Mango Ripeness Based on Yolo

Qiao, T., Luo, X., & Zhu, X. (2025) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip64458.2025.10898109 · Cited by 1

Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images

Mantegna, M., Tronchin, L., Tortora, M., & Soda, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87660-8_7 · Cited by 1

Improved Extrapolation Performance via Manifold Representation and Data Augmentation for Data-Driven Reduced-Order Model

Cheon, S., Kim, H.-J., Ryu, S., Cho, H., & Lee, H. (2025) · Multiscale Science and Engineering · doi:10.1007/s42493-025-00130-4 · Cited by 1

Classification of Multiclass Pap Smear Image and Asymmetric Characterization for Prediction of Severity of Cervical Abnormality

Roy, S. D., Mohanta, A., Edema, T., Nath, N., & Bhowmik, M. K. (2025) · International Journal of Biomedical Imaging · doi:10.1155/ijbi/9860677 · Cited by 2

Binary Classification of Coral Reefs Using Deep Learning for Enhanced Monitoring

Ogidi, M. S., & Şah, M. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101789 · Cited by 3

Differentiable Style Searching: An Online Automatic Data Augmentation Method

Luo, Y., & Yu, J. (2023) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2023.19348 · Cited by 1

Development of vegetation mapping with deep convolutional neural network

Suh, S.-H., Jhang, J.-E., Won, K., Shin, S.-Y., & Sung, C. O. (2018) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3264746.3264791 · Cited by 1

CloseUp—A Community-Driven Live Online Search Engine

von der Weth, C., Abdul, A., Kashyap, A. R., & Kankanhalli, M. (2019) · ACM Transactions on Internet Technology · doi:10.1145/3301442 · Cited by 1

Impact of Video Compression and Multimodal Embedding on Scene Description

Lee, J. Y. (2019) · Electronics · doi:10.3390/electronics8090963 · Cited by 1

The Application of Deep Learning in Marine Sciences

Martín-Abadal, M., Ruiz‐Frau, A., Hinz, H., & González-Cid, Y. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-31760-7_7 · Cited by 2

Data Augmentation Using GANs for 3D Applications

Maniadis, I., Solachidis, V., Vretos, N., & Daras, P. (2020) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-5225-5294-9.ch011 · Cited by 1

A New Backbone Network for Instance Segmentation: Application on a Semiconductor Process Inspection

Han, J., & Hong, S. (2020) · IEEE Access · doi:10.1109/access.2020.3039356 · Cited by 2

Approaches Of Deep Learning In Persuading The Contemporary Society For The Adoption Of New Trend Of AI Systems: A Review

Abu, S. D., & Mohamad, F. S. (2020) · International journal of scientific and technology research · Cited by 1

Microscopic Image Augmentation Using an Enhanced WGAN

Xu, H., Li, C., Zhang, J., Li, Z., Sun, C., & Zhao, X. (2020) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3451421.3451431 · Cited by 1

IDALib: a Python library for efficient image data augmentation

Vila-Blanco, N., Vilas, R. R., & Carreira, M. J. (2022) · European Workshop on Visual Information Processing · doi:10.1109/euvip53989.2022.9922880 · Cited by 1

Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States

Karale, Y., & Yuan, M. (2023) · Frontiers in Remote Sensing · doi:10.3389/frsen.2023.1041466 · Cited by 2

Estimating vegetation index for outdoor free-range pig production using YOLO

Oh, S.-H., Park, H., & Park, J. (2023) · Journal of Animal Science and Technology · doi:10.5187/jast.2023.e41 · Cited by 1

Imitation Learning through Image Augmentation Using Enhanced Swin Transformer Model in Remote Sensing

Park, Y., & Sung, Y. (2023) · Remote Sensing · doi:10.3390/rs15174147 · Cited by 1

Infrared time-sensitive target data augmentation algorithm based on generative model

Wang, S., Yang, X., Lu, R., Li, Q.-G., Fan, J., & Zhu, Z.-J. (2023) · AOPC: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications · doi:10.1117/12.3000067 · Cited by 1

ADA: A Game-Theoretic Perspective on Data Augmentation for Object DetectionPreprint

Behpour, S., Kitani, K., & Ziebart, B. D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07735 · Cited by 1

Multi-stage domain adversarial style reconstruction for cytopathological image stain normalizationPreprint

XiHao, C., Yu, J., Chen, L., Zeng, S., Liu, X., & Cheng, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05184 · Cited by 1

Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition ModelsPreprint

Siddiqui, S., Sizikova, E., Roig, G., Majaj, N. J., & Pelli, D. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00083 · Cited by 1

On the Accuracy of CRNNs for Line-Based OCR: A Multi-Parameter EvaluationPreprint

Liebl, B., & Burghardt, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02777 · Cited by 4

Data Augmentation methods in natural language processing.

Ferreira, T. M. (2021) · doi:10.11606/d.3.2021.tde-04112021-162156 · Cited by 1

Reprint: A Randomized Extrapolation Based on Principal Components for Data AugmentationPreprint

Wei, J., Chen, Q., Peng, P., Guedj, B., & Li, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4149531 · Cited by 1

A Computationally-Inexpensive Strategy in CT Image Data Augmentation for Robust Deep Learning Classification of COVID-19Preprint

Hou, Y., & Navarro‐Cía, M. (2022) · doi:10.36227/techrxiv.20272764 · Cited by 1

Effective Explainable Artificial Intelligence using Visual Explanations in Images

Ibrahim, R. (2022) · doi:10.22215/etd/2022-15159 · Cited by 1

Accurate Left Ventricle Segmentation and Ejection Fraction Estimation with Deep Learning-Based EchocardiographyPreprint

Balasubramani, M., Sung, C., Shieh, M.-Y., Huang, E. P., Abbod, M., & Shieh, J.-S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4629020 · Cited by 1

Automated Segmentation of Cystic Macular Edema in OCT B-Scan Images

Greenwood, L., Habib, M., Hunter, A., & Al-Diri, B. (2018)

Safe Augmentation: Learning Task-Specific Transformations from DataPreprint

Baran, I., Kupyn, O., & Kravchenko, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12896 · Cited by 4

An Approach to Case-Based Reasoning Based on Local Enrichment of the Case Base

Lepage, Y., & Lieber, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29249-2_16

Real-time interactive magnetic resonance (MR) temperature imaging in both aqueous and adipose tissues using cascaded deep neural networks for MR-guided focused ultrasound surgery (MRgFUS)Preprint

Kim, J., Jeong, Y.-J., Chung, H.-J., Lee, C., & Oh, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10995

Superposition as Data Augmentation using LSTM and HMM in Small Training SetsPreprint

Sivaswamy, A., & Pavlovskiy, E. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10881

Deep Convolutional Neural Networks Based on Image Data Augmentation for Visual Object Recognition

Jayech, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33607-3_51

SIMULATION OF IMAGE DATA TO SUPPORT THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS FOR OBJECTS RECOGNITION

Borstell, H., & Nonnen, J. (2019) · Advanced Logistic Systems - Theory and Practice · doi:10.32971/als.2019.010

Optimizing Traditional Transforms for Vehicular Network Sign Classification

Cheng, C., Shen, Y., & Liu, P. (2019) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/massw.2019.00033

Self-Supervised Encoder for Fault Prediction in Electrochemical CellsPreprint

Marcos, D. B., Yacout, S., & Berriah, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13492

Robustness and Overfitting Behavior of Implicit Background ModelsPreprint

Liu, S., Lehman, C., & AlRegib, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09306

Low-Shot Machine Learning for Medical Image Classification

Yip, C. M., Sun, Y., Wang, L., & Wang, Y. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-91683-0_3

On tuning deep learning models: a data mining perspectivePreprint

Öztürk, M. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09857

Video Analytics Solution for COVID-19

Choudhary, P. G. D. B. G. A. V. (2020) · International Journal for Modern Trends in Science and Technology · doi:10.46501/ijmtst061227

Cyclic orthogonal convolutions for long-range integration of featuresPreprint

Freddi, F., Garcia, J., Bromberg, M., Jalali, S., Shiu, D.-S., Chua, A., & Bernacchia, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06462

Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach

Byamugisha, J. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.34

Food Ordering Web Application for the Fitness freaks

Gupta, T. G. M. M. G. A. D. B. (2020) · International Journal for Modern Trends in Science and Technology · doi:10.46501/ijmtst061286

Data Augmentation for Foreign Material Discrimination using Deep Learning

Teragaki, T., Kawahito, S., Kimura, F., & Honda, O. (2020) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai50415.2020.00126

Improved Image Semantic Segmentation Based on Cascade Data Augmentation

Sediqi, K. M., & Lee, H. J. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00296

A Distribution-Aware Training Scheme for Learned Indexes

Wang, Y., Tang, C., & Yao, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85899-5_11

Generatively Augmented Neural Network Watchdog for Image Classification Networks

Bui, J. M., Amigo, G., & Marks, R. J. (2023) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc57344.2023.10099329

Intelligent fire detection device

da Silva, L. T., Dias, J. T., & Cunha, M. (2021) · Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · doi:10.14209/sbrt.2021.1570722958

An Intelligent Vehicle Oriented EMC Fault Shooting Method Based on Semi-supervised Learning

Chen, L., Chai, Y., Zeng, L., Mu, J., Han, Q., & Ye, L. (2021) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/ic-nidc54101.2021.9660595

Exploring Deep Neural Networks for Plant Image Classification

Bafandkar, M. (2022) · doi:10.36939/ir.202205031043

A Computationally-Inexpensive Strategy in CT Image Data Augmentation for Robust Deep Learning Classification of COVID-19Preprint

Hou, Y., & Navarro‐Cía, M. (2022) · doi:10.36227/techrxiv.20272764.v1

Fully Synthetic Videos and Random Background-Pasting Method for Flame SegmentationPreprint

Jia, Y., Zhang, X., Kuang, Y., Chen, Y., Wang, G., & Zhang, Q. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4123022

Beyin Bilgisayarlı Tomografi Görüntülerinde Yapay Zeka Tabanlı Beyin Damar Hastalıkları Tespiti

KARATAŞ, A. F., Doğan, V., & Kılıç, V. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1176648

Detection of tectonic and volcanic deformation as anomalies in InSAR: deep-learning tailored to differential dataPreprint

Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022) · doi:10.1002/essoar.10512903.1

Implementing Computer Vision Techniques to Recognize American Sign Language (ASL) Hand SignalsPreprint

Mohd, T. K., Grande, A. M., Ayala, R. E., & Isteefano, S. (2022) · Preprints.org · doi:10.20944/preprints202212.0478.v1

Augmentation Based Synthetic Sampling and Ensemble Techniques for Imbalanced Data Classification

Asefaw, W. M., Shang, R., Okoth, M. A., & Jiao, L. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-14903-0_15

Measuring the Effect of Data Augmentation Methods for Improving the Success of Convolutional Neural Network

Uçar, K., & Koçer, H. E. (2021) · Gazi Journal of Engineering Sciences · doi:10.30855/gmbd.0705031

Multimodal Adversarial Learning

Osahor, U. (2022) · doi:10.33915/etd.11546

Recognition of Facial Expressions Using Convolutional Neural Networks

Cabezuelo, A. S. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-19-8742-7_5

Acoustic Bandwidth Extension by Audio Deep Residual U-Net

Ou, L., & Chen, Y. (2022) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp55539.2022.10050671

Rice Diseases Recognition Using Transfer Learning from Pre-trained CNN Model

Hamhongsa, W., Wiangsripanawan, R., & Thorncharoensri, P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-30474-3_16 · Cited by 1

AI-Supported Assessment of Load Safety

Schöning, J., & Kruse, N. (2023) · VDI Verlag eBooks · doi:10.51202/9783181024171-67

Explicando as decisões com IAs: Demonstrando sua aplicação em imagens médicas

Santos, E., Santos, J. D., Vogado, L. H. S., Sousa, L., Soares, H., & Veras, R. D. M. S. (2023) · doi:10.5753/sbc.12346.2.3

Comparison of estimating vegetation index for outdoor free-range pig production using convolutional neural networks

Oh, S.-H., Park, H., & Park, J. (2023) · Journal of Animal Science and Technology · doi:10.5187/jast.2023.e81

Detecção Automática de Doenças Oculares em Imagens de Tomografia de Coerência Óptica

Carvalho, C. H. R., Pinheiro, A. M. S., Veras, R. D. M. S., & Silva, R. (2023) · doi:10.5753/sbcas.2023.230144

Real-time traffic sign detection network based on Swin TransformerPreprint

Zhu, W., Yue, Y., Zheng, Y., Chen, Y., & Huang, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3299732/v1

Performance Evaluation of ResNet-based Pneumonia Detection Model with the Small Number of Layers Using Chest X-ray Images

Choi, Y., & Lee, S. (2023) · Bangsaseon gisul gwahak/Journal of radiological science and technology · doi:10.17946/jrst.2023.46.4.277

Invariant Convolutional Networks

Lébl, M., & Flusser, J. (2023) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta59101.2023.10319998

Image-to-image Translation for Enlargement of Aircraft Skin Defect Datasets

Kasule, A. N., Ding, M., Xu, Y., & Xu, J. (2023) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit58768.2023.10351722

Generalizing Clustering Inferences with ML Augmentation of Ordinal Survey Data

Kumar, B., & Kumar, R. (2024) · Computer Science · doi:10.7494/csci.2024.25.1.5685

Comparative analysis of data augmentation methods for image modality

Bokhonko, A., Melnykova, N., & Patereha, Y. (2024) · Scientific journal of the Ternopil national technical university · doi:10.33108/visnyk_tntu2024.01.016

AI-based image processing techniques for the automatic segmentation of human organs

Gupta, S., & Koul, A. (2024) · Advances in computers · doi:10.1016/bs.adcom.2024.03.001

Semantic Segmentation Using Generative Knowledge Distillation for Crack DetectionPreprint

Shim, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4836215

An Improved Method for Retrieving Fashion Images Based on Parsing the Image Contents

Tahseen, F., Talib, A., & Al-Ubaidi, D. A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-62814-6_21

Bibliography

Martinez-Ramon, M., Ajith, M., & Kurup, A. R. (2024) · Wiley eBooks · doi:10.1002/9781119861898.biblio

Blocking harmful images with a deep learning based next generation firewall

BAYSAL, K. (2024) · Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi · doi:10.14744/sigma.2024.000093

Orthogonal Synthesis: Data Augmentation for Multimodal Sentiment Analysis

Zheng, T., & Liu, W. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650640

Training Fully Convolutional Neural Networks for Lightweight, Non-Critical Instance Segmentation Applications

Veganzones, M. A., Cisnal, A., López, E. D. L. F., & Fraile, J. C. (2024) · Applied Sciences · doi:10.3390/app142311357

Application of Generative Adversarial Networks on Rnaseq Data to Uncover COVID-19 Severity BiomarkersPreprint

Kalimumbalo, Y. K., Macharia, R. W., & Wagacha, P. W. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5038981

Artery Vein Segmentation in Handheld Fundus Camera Retinal Images and leveraging Cross Entropy for improved Semantic performance

Yohannan, R. P., David, S. S., Vijayasenan, D., Chowdary, R. T., Menon, R. G., & Menon, S. G. (2024) · IEEE International Conference on Computer Vision and Machine Intelligence (CVMI) · doi:10.1109/cvmi61877.2024.10782781

Improving Deep Learning using Generic Data AugmentationPreprint

Taylor, L. A., & Nitschke, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.06020 · Cited by 159

Data Augmentation For Deep Learning Using Generative Adversarial Networks

Yorioka, D., Kang, H., & Iwamura, K. (2020) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce50665.2020.9291963 · Cited by 28

Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

Takahashi, R., Matsubara, T., & Uehara, K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2935128 · Cited by 477

Convolutional neural network for data augmentation

Kim, R., & White, E. (2024) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2024.13.2.0528 · Cited by 6

Effect of Data Augmentation on the Accuracy of Convolutional Neural Networks

Bhuse, P., Singh, B., & Raut, P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-0739-4_33 · Cited by 8