Artículos de revistas sobre el tema "Classification based on generative models"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Classification based on generative models".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Cazzanti, Luca, Maya R. Gupta y Anjali J. Koppal. "Generative models for similarity-based classification". Pattern Recognition 41, n.º 7 (julio de 2008): 2289–97. http://dx.doi.org/10.1016/j.patcog.2008.01.005.
Texto completoWei, Wei, Jun Fang, Ning Yang, Qi Li, Lin Hu, Lanbo Zhao y Jie Han. "AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification". International Journal of Molecular Sciences 25, n.º 13 (25 de junio de 2024): 6940. http://dx.doi.org/10.3390/ijms25136940.
Texto completoGopal, Narendra y Sivakumar D. "DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION". ICTACT Journal on Image and Video Processing 13, n.º 01 (1 de agosto de 2022): 2786–90. http://dx.doi.org/10.21917/ijivp.2022.0396.
Texto completoShastry, K. Aditya, B. A. Manjunatha, T. G. Mohan Kumar y D. U. Karthik. "Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset". Journal of ICT Research and Applications 17, n.º 2 (31 de agosto de 2023): 181–200. http://dx.doi.org/10.5614/itbj.ict.res.appl.2023.17.2.4.
Texto completoEkolle, Zie Eya y Ryuji Kohno. "GenCo: A Generative Learning Model for Heterogeneous Text Classification Based on Collaborative Partial Classifications". Applied Sciences 13, n.º 14 (14 de julio de 2023): 8211. http://dx.doi.org/10.3390/app13148211.
Texto completoZhai, Junhai, Jiaxing Qi y Chu Shen. "Binary imbalanced data classification based on diversity oversampling by generative models". Information Sciences 585 (marzo de 2022): 313–43. http://dx.doi.org/10.1016/j.ins.2021.11.058.
Texto completoKim, Eunbeen, Jaeuk Moon, Jonghwa Shim y Eenjun Hwang. "DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification". Sensors 23, n.º 4 (10 de febrero de 2023): 2024. http://dx.doi.org/10.3390/s23042024.
Texto completoKannan, K. Gokul y T. R. Ganesh Babu. "Semi Supervised Generative Adversarial Network for Automated Glaucoma Diagnosis with Stacked Discriminator Models". Journal of Medical Imaging and Health Informatics 11, n.º 5 (1 de mayo de 2021): 1334–40. http://dx.doi.org/10.1166/jmihi.2021.3787.
Texto completoChen, Zirui. "Diffusion Models-based Data Augmentation for the Cell Cycle Phase Classification". Journal of Physics: Conference Series 2580, n.º 1 (1 de septiembre de 2023): 012001. http://dx.doi.org/10.1088/1742-6596/2580/1/012001.
Texto completoBhavani, N. Sree, G. Narendra Babu Reddy, Y. Sravani Devi, M. Bhavani, P. Chandana Reddy y V. Abhignya Reddy. "Generative Data Augmentation and ARMD Classification". International Journal for Research in Applied Science and Engineering Technology 11, n.º 6 (30 de junio de 2023): 3662–67. http://dx.doi.org/10.22214/ijraset.2023.54178.
Texto completoWang, Chuantao, Xuexin Yang y Linkai Ding. "Imbalanced sentiment classification based on sequence generative adversarial nets". Journal of Intelligent & Fuzzy Systems 39, n.º 5 (19 de noviembre de 2020): 7909–19. http://dx.doi.org/10.3233/jifs-201370.
Texto completoHassani, Hossein, Roozbeh Razavi-Far, Mehrdad Saif y Vasile Palade. "Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems". Sensors 21, n.º 15 (30 de julio de 2021): 5173. http://dx.doi.org/10.3390/s21155173.
Texto completoCao, Zhiyi, Lei Shi, Wei Wang y Shaozhang Niu. "Facial Pose and Expression Transfer Based on Classification Features". Electronics 12, n.º 8 (7 de abril de 2023): 1756. http://dx.doi.org/10.3390/electronics12081756.
Texto completoWon, K. J., C. Saunders y A. Prügel-Bennett. "Evolving Fisher Kernels for Biological Sequence Classification". Evolutionary Computation 21, n.º 1 (marzo de 2013): 83–105. http://dx.doi.org/10.1162/evco_a_00065.
Texto completoMiller, David J., Jayaram Raghuram, George Kesidis y Christopher M. Collins. "Improved Generative Semisupervised Learning Based on Finely Grained Component-Conditional Class Labeling". Neural Computation 24, n.º 7 (julio de 2012): 1926–66. http://dx.doi.org/10.1162/neco_a_00284.
Texto completoBandi, Ajay, Pydi Venkata Satya Ramesh Adapa y Yudu Eswar Vinay Pratap Kumar Kuchi. "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges". Future Internet 15, n.º 8 (31 de julio de 2023): 260. http://dx.doi.org/10.3390/fi15080260.
Texto completoZhou, Kun, Wenyong Wang, Teng Hu y Kai Deng. "Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks". Sensors 20, n.º 24 (16 de diciembre de 2020): 7211. http://dx.doi.org/10.3390/s20247211.
Texto completoLv, Yancheng, Lin Lin, Jie Liu, Hao Guo y Changsheng Tong. "Research on Imbalanced Data Classification Based on Classroom-Like Generative Adversarial Networks". Neural Computation 34, n.º 4 (23 de marzo de 2022): 1045–73. http://dx.doi.org/10.1162/neco_a_01470.
Texto completoZhang, Xia y Mingyu Ma. "Research on sEMG Feature Generation and Classification Performance Based on EBGAN". Electronics 12, n.º 4 (20 de febrero de 2023): 1040. http://dx.doi.org/10.3390/electronics12041040.
Texto completoLi, Bohan, Xiao Xu, Xinghao Wang, Yutai Hou, Yunlong Feng, Feng Wang, Xuanliang Zhang, Qingfu Zhu y Wanxiang Che. "Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de marzo de 2024): 3018–27. http://dx.doi.org/10.1609/aaai.v38i4.28084.
Texto completoZhang, Zhaohui, Lijun Yang, Ligong Chen, Qiuwen Liu, Ying Meng, Pengwei Wang y Maozhen Li. "A generative adversarial network–based method for generating negative financial samples". International Journal of Distributed Sensor Networks 16, n.º 2 (febrero de 2020): 155014772090705. http://dx.doi.org/10.1177/1550147720907053.
Texto completoSheeny, Marcel, Andrew Wallace y Sen Wang. "RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets". Applied Sciences 10, n.º 11 (2 de junio de 2020): 3861. http://dx.doi.org/10.3390/app10113861.
Texto completoGao, Dan, Xiaofang Wu, Zhijin Wen, Yue Xu y Zhengchao Chen. "Few-shot SAR vehicle target augmentation based on generative adversarial networks". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (9 de mayo de 2024): 83–90. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-83-2024.
Texto completoLee, Donghoun. "Driving Safety Area Classification for Automated Vehicles Based on Data Augmentation Using Generative Models". Sustainability 16, n.º 11 (21 de mayo de 2024): 4337. http://dx.doi.org/10.3390/su16114337.
Texto completoWu, Jheng-Long y Shuoyen Huang. "Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data". Applied Sciences 12, n.º 21 (29 de octubre de 2022): 10964. http://dx.doi.org/10.3390/app122110964.
Texto completoŠkorić, Mihailo, Miloš Utvić y Ranka Stanković. "Transformer-Based Composite Language Models for Text Evaluation and Classification". Mathematics 11, n.º 22 (16 de noviembre de 2023): 4660. http://dx.doi.org/10.3390/math11224660.
Texto completoJie, Zhideng, Hong Zhang, Kaixuan Li, Xiao Xie y Aopu Shi. "Image Enhancement of Steel Plate Defects Based on Generative Adversarial Networks". Electronics 13, n.º 11 (22 de mayo de 2024): 2013. http://dx.doi.org/10.3390/electronics13112013.
Texto completoChen, Yushi, Lingbo Huang, Lin Zhu, Naoto Yokoya y Xiuping Jia. "Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning". Remote Sensing 11, n.º 22 (18 de noviembre de 2019): 2690. http://dx.doi.org/10.3390/rs11222690.
Texto completoChatterjee, Kalyan, M. Raju, N. Selvamuthukumaran, M. Pramod, B. Krishna Kumar, Anjan Bandyopadhyay y Saurav Mallik. "HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model". Information 15, n.º 2 (4 de febrero de 2024): 85. http://dx.doi.org/10.3390/info15020085.
Texto completoLi, Yuanming, Bonhwa Ku, Shou Zhang, Jae-Kwang Ahn y Hanseok Ko. "Seismic Data Augmentation Based on Conditional Generative Adversarial Networks". Sensors 20, n.º 23 (30 de noviembre de 2020): 6850. http://dx.doi.org/10.3390/s20236850.
Texto completoLiu, Kun, Xiaolin Ning y Sidong Liu. "Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling". Sensors 22, n.º 24 (17 de diciembre de 2022): 9967. http://dx.doi.org/10.3390/s22249967.
Texto completoShaik, Abdul Lateef Haroon Phulara, Monica Komala Manoharan, Alok Kumar Pani, Raji Reddy Avala y Chien-Ming Chen. "Gaussian Mutation–Spider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification". Remote Sensing 14, n.º 24 (11 de diciembre de 2022): 6279. http://dx.doi.org/10.3390/rs14246279.
Texto completoMounica, Mrs K. V. S. "GAN Based Multi-Class Skin Disease Classification: Deep Learning Approach". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de mayo de 2024): 137–42. http://dx.doi.org/10.22214/ijraset.2024.61366.
Texto completoAlhumaid, Mohammad y Ayman G. Fayoumi. "Transfer Learning-Based Classification of Maxillary Sinus Using Generative Adversarial Networks". Applied Sciences 14, n.º 7 (6 de abril de 2024): 3083. http://dx.doi.org/10.3390/app14073083.
Texto completoSchaudt, Daniel, Christian Späte, Reinhold von Schwerin, Manfred Reichert, Marianne von Schwerin, Meinrad Beer y Christopher Kloth. "A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data". Bioengineering 10, n.º 12 (14 de diciembre de 2023): 1421. http://dx.doi.org/10.3390/bioengineering10121421.
Texto completoElzobi, Moftah y Ayoub Al-Hamadi. "Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting". Sensors 18, n.º 9 (24 de agosto de 2018): 2786. http://dx.doi.org/10.3390/s18092786.
Texto completoLee, Junghyuk, Jun-Hyuk Kim y Jong-Seok Lee. "Demystifying Randomly Initialized Networks for Evaluating Generative Models". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 8482–90. http://dx.doi.org/10.1609/aaai.v37i7.26022.
Texto completoAbedi, Masoud, Lars Hempel, Sina Sadeghi y Toralf Kirsten. "GAN-Based Approaches for Generating Structured Data in the Medical Domain". Applied Sciences 12, n.º 14 (13 de julio de 2022): 7075. http://dx.doi.org/10.3390/app12147075.
Texto completoYou, Yuyang, Xiaoyu Guo, Xuyang Zhong y Zhihong Yang. "A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance". Biomedicines 10, n.º 12 (22 de noviembre de 2022): 3006. http://dx.doi.org/10.3390/biomedicines10123006.
Texto completoCheng, Ruoxi. "Expansion of the CT-scans image set based on the pretrained DCGAN for improving the performance of the CNN". Journal of Physics: Conference Series 2646, n.º 1 (1 de diciembre de 2023): 012015. http://dx.doi.org/10.1088/1742-6596/2646/1/012015.
Texto completoDivyanth, L. G., D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi y Jitendra Paliwal. "Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications". Algorithms 15, n.º 11 (30 de octubre de 2022): 401. http://dx.doi.org/10.3390/a15110401.
Texto completoHsieh, Chen-Chiung, Ti-Yun Hsu y Wei-Hsin Huang. "An Online Rail Track Fastener Classification System Based on YOLO Models". Sensors 22, n.º 24 (17 de diciembre de 2022): 9970. http://dx.doi.org/10.3390/s22249970.
Texto completoWang, Ziyue y Junjun Guo. "Self-adaptive attention fusion for multimodal aspect-based sentiment analysis". Mathematical Biosciences and Engineering 21, n.º 1 (2023): 1305–20. http://dx.doi.org/10.3934/mbe.2024056.
Texto completoAlrashedy, Halima Hamid N., Atheer Fahad Almansour, Dina M. Ibrahim y Mohammad Ali A. Hammoudeh. "BrainGAN: Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models". Sensors 22, n.º 11 (6 de junio de 2022): 4297. http://dx.doi.org/10.3390/s22114297.
Texto completoYan, Yang, Wen Bo Huang, Yun Ji Wang y Na Li. "Image Labeling Model Based on Conditional Random Fields". Advanced Materials Research 756-759 (septiembre de 2013): 3869–73. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3869.
Texto completoAlhoraibi, Lamia, Daniyal Alghazzawi y Reemah Alhebshi. "Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer Authentication". Sensors 24, n.º 2 (19 de enero de 2024): 641. http://dx.doi.org/10.3390/s24020641.
Texto completoBoone, Kyle. "ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning". Astronomical Journal 162, n.º 6 (1 de diciembre de 2021): 275. http://dx.doi.org/10.3847/1538-3881/ac2a2d.
Texto completoYang, Guan, Chao Li, Xiaojun Liu y Guangyou Fang. "A THz Passive Image Generation Method Based on Generative Adversarial Networks". Applied Sciences 12, n.º 4 (14 de febrero de 2022): 1976. http://dx.doi.org/10.3390/app12041976.
Texto completoGaspar, Héléna A., Gilles Marcou, Dragos Horvath, Alban Arault, Sylvain Lozano, Philippe Vayer y Alexandre Varnek. "Generative Topographic Mapping-Based Classification Models and Their Applicability Domain: Application to the Biopharmaceutics Drug Disposition Classification System (BDDCS)". Journal of Chemical Information and Modeling 53, n.º 12 (9 de diciembre de 2013): 3318–25. http://dx.doi.org/10.1021/ci400423c.
Texto completoLu, Zhengdong, Todd K. Leen y Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals". Neural Computation 23, n.º 9 (septiembre de 2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.
Texto completo