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Publication search results
found 231 matches
- 2024
- Haotian Zhang, Lei Zhao, Yuan Jiang:
Triplet Network and Unsupervised-Clustering-Based Zero-Shot Radio Frequency Fingerprint Identification With Extremely Small Sample Size. IEEE Internet Things J. 11(8): 14416-14434 (2024) - 2023
- Penny Johnston, Keiller Nogueira, Kevin Swingler:
GMM-IL: Image Classification Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes. IEEE Access 11: 25492-25501 (2023) - Penny Johnston, Keiller Nogueira, Kevin Swingler:
NS-IL: Neuro-Symbolic Visual Question Answering Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes. IEEE Access 11: 141406-141420 (2023) - Xun Shen, Hampei Sasahara, Masahide Morishita, Jun-ichi Imura, Makito Oku, Kazuyuki Aihara:
Model-Free Dominant Pole Placement for Restabilizing High-Dimensional Network Systems via Small-Sample-Size Data. IEEE Access 11: 45572-45585 (2023) - Yiding Feng, Xiang Feng, Huiqun Yu:
Small-sample size problems solving based on incremental learning: an adaptive Bayesian quadrature approach. Appl. Intell. 53(12): 15174-15187 (2023) - Dávid Abriha, Szilárd Szabó:
Strategies in training deep learning models to extract building from multisource images with small training sample sizes. Int. J. Digit. Earth 16(1): 1707-1724 (2023) - Flavien Collart, Antoine Guisan:
Small to train, small to test: Dealing with low sample size in model evaluation. Ecol. Informatics 75: 102106 (2023) - Enlin Li, Liwei Wang, Qiuju Xie, Rui Gao, Zhongbin Su, Yonggang Li:
A novel deep learning method for maize disease identification based on small sample-size and complex background datasets. Ecol. Informatics 75: 102011 (2023) - Li Ma, Shuyue Li, Zhiyong Zhou, Yafeng Yao, Qian Du:
Semantic Segmentation Network for Classification of Hyperspectral Images With Small Size Samples. IEEE Geosci. Remote. Sens. Lett. 20: 1-5 (2023) - Xiaobin Xu, Haojie Zhang, Yingying Ran, Zhiying Tan:
High-Precision Segmentation of Buildings with Small Sample Sizes Based on Transfer Learning and Multi-Scale Fusion. Remote. Sens. 15(9): 2436 (2023) - Albert Selebea Lutakamale, Yona Zakaria Manyesela:
Machine Learning-Based Fingerprinting Positioning in Massive MIMO Networks: Analysis on the Impact of Small Training Sample Size to the Positioning Performance. SN Comput. Sci. 4(3): 286 (2023) - Ran Liu, Cynthia Matuszek, Charles Nicholas:
A PDF Malware Detection Method Using Extremely Small Training Sample Size. DocEng 2023: 27:1-27:4 - Monika Pytlarz, Adrian Onicas, Alessandro Crimi:
Style Transfer Between Microscopy and Magnetic Resonance Imaging Via Generative Adversarial Network in Small Sample Size Settings. ICIP 2023: 1120-1124 - Alibek Zhakubayev, Thomas Andersen, Annie Vesterby, Lene Warner Thorup Boel, Kathleen A. Grant, Urszula Iwaniec, Russell Turner, Erich J. Baker, Mary Lauren Benton:
Image processing approach provides robust feature extraction for classification with small sample sizes. ICISDM 2023: 82-89 - Yu Fu, Yanyan Huang, Shunjie Dong, Yalin Wang, Tianbai Yu, Meng Niu, Cheng Zhuo:
SFCNEXT: A Simple Fully Convolutional Network for Effective Brain Age Estimation with Small Sample Size. ISBI 2023: 1-5 - Hayden Jeune, Niklas Pechan, Sharn-Konet Reitsma, Andreas W. Kempa-Liehr:
Spatial Variation Sequences for Remote Sensing Applications with Small Sample Sizes. PSIVT 2023: 153-166 - Yu Fu, Yanyan Huang, Shunjie Dong, Yalin Wang, Tianbai Yu, Meng Niu, Cheng Zhuo:
SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size. CoRR abs/2305.18771 (2023) - Chandrika Kamath, Juliette S. Franzman, Brian H. Daub:
Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part I - Analysis with a Small Sample Size. CoRR abs/2307.01393 (2023) - Monika Pytlarz, Adrian Onicas, Alessandro Crimi:
Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings. CoRR abs/2310.10414 (2023) - Rallou A. Chatzimichail, Aristides T. Hatjimihail:
Quality control using convolutional neural networks applied to samples of very small size. CoRR abs/2310.10608 (2023) - 2022
- Yanmin Zhu, Tianhao Peng, Shuzhi Su:
Exponential Multi-Modal Discriminant Feature Fusion for Small Sample Size. IEEE Access 10: 14507-14517 (2022) - Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng:
Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation. Briefings Bioinform. 23(1) (2022) - Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng:
Correction to: Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation. Briefings Bioinform. 23(3) (2022) - Fengyi Wang, Yuan Rao, Qing Luo, Xiu Jin, Zhao-Hui Jiang, Wu Zhang, Shaowen Li:
Practical cucumber leaf disease recognition using improved Swin Transformer and small sample size. Comput. Electron. Agric. 199: 107163 (2022) - Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa:
Multi-task manifold learning for small sample size datasets. Neurocomputing 473: 138-157 (2022) - Neofytos Dimitriou, Ognjen Arandjelovic:
Sequential Normalization: Embracing Smaller Sample Sizes for Normalization. Inf. 13(7): 337 (2022) - Heng Xia, Jian Tang, Junfei Qiao, Jian Zhang, Wen Yu:
DF classification algorithm for constructing a small sample size of data-oriented DF regression model. Neural Comput. Appl. 34(4): 2785-2810 (2022) - Xiwei She, Theodore W. Berger, Dong Song:
A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size. Neural Comput. 34(1): 219-254 (2022) - Yen-Kuang Lin, Chen-Yin Lee, Chen-Yueh Chen:
Robustness of autoencoders for establishing psychometric properties based on small sample sizes: results from a Monte Carlo simulation study and a sports fan curiosity study. PeerJ Comput. Sci. 8: e782 (2022) - Yanbing Xu, Yanmei Zhang, Chengcheng Yu, Chao Ji, Tingxuan Yue, Huan Li:
Residual Spatial Attention Kernel Generation Network for Hyperspectral Image Classification With Small Sample Size. IEEE Trans. Geosci. Remote. Sens. 60: 1-14 (2022)
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