Статті в журналах з теми "Limited training data"
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Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, and Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.
Повний текст джерелаMcLaughlin, Niall, Ji Ming, and Danny Crookes. "Robust Multimodal Person Identification With Limited Training Data." IEEE Transactions on Human-Machine Systems 43, no. 2 (March 2013): 214–24. http://dx.doi.org/10.1109/tsmcc.2012.2227959.
Повний текст джерелаZhang, Mingyang, Berrak Sisman, Li Zhao, and Haizhou Li. "DeepConversion: Voice conversion with limited parallel training data." Speech Communication 122 (September 2020): 31–43. http://dx.doi.org/10.1016/j.specom.2020.05.004.
Повний текст джерелаQian, Tieyun, Bing Liu, Li Chen, Zhiyong Peng, Ming Zhong, Guoliang He, Xuhui Li, and Gang Xu. "Tri-Training for authorship attribution with limited training data: a comprehensive study." Neurocomputing 171 (January 2016): 798–806. http://dx.doi.org/10.1016/j.neucom.2015.07.064.
Повний текст джерелаSaunders, Sara L., Ethan Leng, Benjamin Spilseth, Neil Wasserman, Gregory J. Metzger, and Patrick J. Bolan. "Training Convolutional Networks for Prostate Segmentation With Limited Data." IEEE Access 9 (2021): 109214–23. http://dx.doi.org/10.1109/access.2021.3100585.
Повний текст джерелаZhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court, and Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data." Medical Physics 48, no. 4 (February 19, 2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.
Повний текст джерелаHoffbeck, J. P., and D. A. Landgrebe. "Covariance matrix estimation and classification with limited training data." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 7 (July 1996): 763–67. http://dx.doi.org/10.1109/34.506799.
Повний текст джерелаCui, Kaiwen, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan, and Shijian Lu. "GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 499–507. http://dx.doi.org/10.1609/aaai.v36i1.19928.
Повний текст джерелаKim, June-Woo, and Ho-Young Jung. "End-to-end speech recognition models using limited training data*." Phonetics and Speech Sciences 12, no. 4 (December 2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.
Повний текст джерелаTambouratzis, George, and Marina Vassiliou. "Swarm Algorithms for NLP - The Case of Limited Training Data." Journal of Artificial Intelligence and Soft Computing Research 9, no. 3 (July 1, 2019): 219–34. http://dx.doi.org/10.2478/jaiscr-2019-0005.
Повний текст джерелаLiu, Weijian, Zhaojian Zhang, Jun Liu, Zheran Shang, and Yong-Liang Wang. "Detection of a rank-one signal with limited training data." Signal Processing 186 (September 2021): 108120. http://dx.doi.org/10.1016/j.sigpro.2021.108120.
Повний текст джерелаPark, Ji-Hoon, Seung-Mo Seo, and Ji-Hee Yoo. "SAR ATR for Limited Training Data Using DS-AE Network." Sensors 21, no. 13 (July 1, 2021): 4538. http://dx.doi.org/10.3390/s21134538.
Повний текст джерелаKim, June-Woo, and Ho-Young Jung. "End-to-end speech recognition models using limited training data*." Phonetics and Speech Sciences 12, no. 4 (December 2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.
Повний текст джерелаGhorbandoost, Mostafa, Abolghasem Sayadiyan, Mohsen Ahangar, Hamid Sheikhzadeh, Abdoreza Sabzi Shahrebabaki, and Jamal Amini. "Voice conversion based on feature combination with limited training data." Speech Communication 67 (March 2015): 113–28. http://dx.doi.org/10.1016/j.specom.2014.12.004.
Повний текст джерелаYAMASHITA, Masaru. "Acoustic HMMs to Detect Abnormal Respiration with Limited Training Data." IEICE Transactions on Information and Systems E106.D, no. 3 (March 1, 2023): 374–80. http://dx.doi.org/10.1587/transinf.2022edp7068.
Повний текст джерелаWang, Jingjing, Zheng Liu, Rong Xie, and Lei Ran. "Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data." Remote Sensing 13, no. 4 (February 18, 2021): 750. http://dx.doi.org/10.3390/rs13040750.
Повний текст джерелаXu, Ning, Yibing Tang, Jingyi Bao, Aiming Jiang, Xiaofeng Liu, and Zhen Yang. "Voice conversion based on Gaussian processes by coherent and asymmetric training with limited training data." Speech Communication 58 (March 2014): 124–38. http://dx.doi.org/10.1016/j.specom.2013.11.005.
Повний текст джерелаWang, S. L., A. W. C. Liew, W. H. Lau, and S. H. Leung. "An Automatic Lipreading System for Spoken Digits With Limited Training Data." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 12 (December 2008): 1760–65. http://dx.doi.org/10.1109/tcsvt.2008.2004924.
Повний текст джерелаCreswell, Antonia, Alison Pouplin, and Anil A. Bharath. "Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data." IET Computer Vision 12, no. 8 (September 12, 2018): 1105–11. http://dx.doi.org/10.1049/iet-cvi.2018.5243.
Повний текст джерелаAghamaleki, Javad Abbasi, and Vahid Ashkani Chenarlogh. "Multi-stream CNN for facial expression recognition in limited training data." Multimedia Tools and Applications 78, no. 16 (April 25, 2019): 22861–82. http://dx.doi.org/10.1007/s11042-019-7530-7.
Повний текст джерелаCrowson, Merry, Ron Hagensieker, and Björn Waske. "Mapping land cover change in northern Brazil with limited training data." International Journal of Applied Earth Observation and Geoinformation 78 (June 2019): 202–14. http://dx.doi.org/10.1016/j.jag.2018.10.004.
Повний текст джерелаJannati, Mohammad Javad, and Abolghasem Sayadiyan. "Part-Syllable Transformation-Based Voice Conversion with Very Limited Training Data." Circuits, Systems, and Signal Processing 37, no. 5 (August 30, 2017): 1935–57. http://dx.doi.org/10.1007/s00034-017-0639-x.
Повний текст джерелаDemir, Begum, Francesca Bovolo, and Lorenzo Bruzzone. "Classification of Time Series of Multispectral Images With Limited Training Data." IEEE Transactions on Image Processing 22, no. 8 (August 2013): 3219–33. http://dx.doi.org/10.1109/tip.2013.2259838.
Повний текст джерелаLang, Yue, Qing Wang, Yang Yang, Chunping Hou, Yuan He, and Jinchen Xu. "Person identification with limited training data using radar micro‐Doppler signatures." Microwave and Optical Technology Letters 62, no. 3 (November 2019): 1060–68. http://dx.doi.org/10.1002/mop.32125.
Повний текст джерелаKhezri, Shirin, Jafar Tanha, Ali Ahmadi, and Arash Sharifi. "STDS: self-training data streams for mining limited labeled data in non-stationary environment." Applied Intelligence 50, no. 5 (January 21, 2020): 1448–67. http://dx.doi.org/10.1007/s10489-019-01585-3.
Повний текст джерелаTang, Yehui, Shan You, Chang Xu, Jin Han, Chen Qian, Boxin Shi, Chao Xu, and Changshui Zhang. "Reborn Filters: Pruning Convolutional Neural Networks with Limited Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5972–80. http://dx.doi.org/10.1609/aaai.v34i04.6058.
Повний текст джерелаDuong, Huu-Thanh, Tram-Anh Nguyen-Thi, and Vinh Truong Hoang. "Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks." Complexity 2022 (June 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/3188449.
Повний текст джерелаJackson, Q., and D. A. Landgrebe. "An adaptive classifier design for high-dimensional data analysis with a limited training data set." IEEE Transactions on Geoscience and Remote Sensing 39, no. 12 (2001): 2664–79. http://dx.doi.org/10.1109/36.975001.
Повний текст джерелаChen, Shangyu, Wenya Wang, and Sinno Jialin Pan. "Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3329–36. http://dx.doi.org/10.1609/aaai.v33i01.33013329.
Повний текст джерелаMamyrbayev, O. Zh, M. Othman, A. T. Akhmediyarova, A. S. Kydyrbekova, and N. O. Mekebayev. "VOICE VERIFICATION USING I-VECTORS AND NEURAL NETWORKS WITH LIMITED TRAINING DATA." BULLETIN 3, no. 379 (June 15, 2019): 36–43. http://dx.doi.org/10.32014/2019.2518-1467.66.
Повний текст джерелаZiv, J. "An efficient universal prediction algorithm for unknown sources with limited training data." IEEE Transactions on Information Theory 48, no. 6 (June 2002): 1690–93. http://dx.doi.org/10.1109/tit.2002.1003847.
Повний текст джерелаOh, Yujin, Sangjoon Park, and Jong Chul Ye. "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets." IEEE Transactions on Medical Imaging 39, no. 8 (August 2020): 2688–700. http://dx.doi.org/10.1109/tmi.2020.2993291.
Повний текст джерелаHou, Yuchao, Ting Xu, Hongping Hu, Peng Wang, Hongxin Xue, and Yanping Bai. "MdpCaps-Csl for SAR Image Target Recognition With Limited Labeled Training Data." IEEE Access 8 (2020): 176217–31. http://dx.doi.org/10.1109/access.2020.3026469.
Повний текст джерелаGe, Zhiqiang, Zhihuan Song, and Furong Gao. "Self-Training Statistical Quality Prediction of Batch Processes with Limited Quality Data." Industrial & Engineering Chemistry Research 52, no. 2 (December 28, 2012): 979–84. http://dx.doi.org/10.1021/ie300616s.
Повний текст джерелаKaewtip, Kantapon, Abeer Alwan, and Charles Taylor. "Robust Hidden Markov Models for limited training data for birdsong phrase classification." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3725–26. http://dx.doi.org/10.1121/1.4988171.
Повний текст джерелаKrishnagopal, Sanjukta, Yiannis Aloimonos, and Michelle Girvan. "Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach." Complexity 2018 (November 1, 2018): 1–15. http://dx.doi.org/10.1155/2018/6953836.
Повний текст джерелаZiel, Florian. "Load Nowcasting: Predicting Actuals with Limited Data." Energies 13, no. 6 (March 20, 2020): 1443. http://dx.doi.org/10.3390/en13061443.
Повний текст джерелаSenchenkov, Valentin, Damir Absalyamov, and Dmitriy Avsyukevich. "Diagnostics of life support systems with limited statistical data on failures." E3S Web of Conferences 140 (2019): 05002. http://dx.doi.org/10.1051/e3sconf/201914005002.
Повний текст джерелаBardis, Michelle, Roozbeh Houshyar, Chanon Chantaduly, Alexander Ushinsky, Justin Glavis-Bloom, Madeleine Shaver, Daniel Chow, Edward Uchio, and Peter Chang. "Deep Learning with Limited Data: Organ Segmentation Performance by U-Net." Electronics 9, no. 8 (July 26, 2020): 1199. http://dx.doi.org/10.3390/electronics9081199.
Повний текст джерелаHe, Qiuchen, Shaobo Li, Chuanjiang Li, Junxing Zhang, Ansi Zhang, and Peng Zhou. "A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–14. http://dx.doi.org/10.1155/2022/3024590.
Повний текст джерелаVidal, Joel, Guillem Vallicrosa, Robert Martí, and Marc Barnada. "Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data." Sensors 23, no. 4 (February 8, 2023): 1898. http://dx.doi.org/10.3390/s23041898.
Повний текст джерелаPark, Sangyong, Jaeseon Kim, and Yong Seok Heo. "Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data." Sensors 22, no. 7 (March 29, 2022): 2623. http://dx.doi.org/10.3390/s22072623.
Повний текст джерелаGimeno, Pablo, Victoria Mingote, Alfonso Ortega, Antonio Miguel, and Eduardo Lleida. "Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data." IEEE Signal Processing Letters 28 (2021): 1135–39. http://dx.doi.org/10.1109/lsp.2021.3084501.
Повний текст джерелаJafaryani, Mohamadreza, Hamid Sheikhzadeh, and Vahid Pourahmadi. "Parallel voice conversion with limited training data using stochastic variational deep kernel learning." Engineering Applications of Artificial Intelligence 115 (October 2022): 105279. http://dx.doi.org/10.1016/j.engappai.2022.105279.
Повний текст джерелаLi, Hai, Wenyu Song, Weijian Liu, and Renbiao Wu. "Moving target detection with limited training data based on the subspace orthogonal projection." IET Radar, Sonar & Navigation 12, no. 7 (July 2018): 679–84. http://dx.doi.org/10.1049/iet-rsn.2017.0449.
Повний текст джерелаZhang, Mengmeng, Wei Li, Ran Tao, and Song Wang. "Transfer Learning for Optical and SAR Data Correspondence Identification With Limited Training Labels." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 1545–57. http://dx.doi.org/10.1109/jstars.2020.3044643.
Повний текст джерелаDavari, Amirabbas, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier, and Christian Riess. "GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data." IEEE Geoscience and Remote Sensing Letters 15, no. 6 (June 2018): 942–46. http://dx.doi.org/10.1109/lgrs.2018.2817361.
Повний текст джерелаSun, Yuanshuang, Yinghua Wang, Hongwei Liu, Ning Wang, and Jian Wang. "SAR Target Recognition With Limited Training Data Based on Angular Rotation Generative Network." IEEE Geoscience and Remote Sensing Letters 17, no. 11 (November 2020): 1928–32. http://dx.doi.org/10.1109/lgrs.2019.2958379.
Повний текст джерелаZeng, Dan, Luuk Spreeuwers, Raymond Veldhuis, and Qijun Zhao. "Combined training strategy for low-resolution face recognition with limited application-specific data." IET Image Processing 13, no. 10 (August 22, 2019): 1790–96. http://dx.doi.org/10.1049/iet-ipr.2018.5732.
Повний текст джерелаShin, Hyunkyung, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh, and Honguk Woo. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions." Applied Sciences 12, no. 14 (July 21, 2022): 7317. http://dx.doi.org/10.3390/app12147317.
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