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References

1 
R. Tîrnovan, M. Cristea, 2019, Advanced techniques for fault detection and classification in electrical power transmission systems: An overview, pp. 1-6DOI
2 
Z. Huang, Y. Wei, X. Wang, W. Liu, T. S. Huang, H. Shi, 2020, AlignSeg:Feature-Aligned Segmentation Networks, arXiv preprint arXiv:2003.00872DOI
3 
H. Deng, X. Li, 2022, Anomaly detection via reverse distillation from one-class embedding, pp. 9737-9746Google Search
4 
G. Pereyra, 2017, Regularizing neural networks by penalizing confident output distributions, arXiv preprint arXiv:1701.06548DOI
5 
K. P. Murphy, 2012, Machine Learning: A Probabilistic PerspectiveGoogle Search
6 
S. Wager, S. Wang, P. Liang, 2013, Advances in Neural Information Processing Systems, Vol. 26Google Search
7 
K. He, X. Zhang, S. Ren, J. Sun, 2016, Deep residual learning for image recognition, pp. 770-778Google Search
8 
R. Namdar, 2022, Improving generalization in deep learning models under noisy and small sample conditions via multitask learning, IEEE Access, Vol. 10, pp. 12345-12358Google Search
9 
F. Zhuang, 2021, A comprehensive survey on transfer learning, Proceedings of the IEEE, Vol. 109, No. 1, pp. 43-76DOI
10 
P. Lu, 2021, RW-KD: Sample-wise loss terms re-weighting for knowledge distillation, Findings of ACL: EMNLP, Vol. 2021, pp. 3145-3152DOI
11 
Y. Ren, 2023, Tailoring instructions to student’s learning levels boosts knowledge distillation, arXiv preprint arXiv:2305.09651DOI
12 
A. Romero, 2015, FitNets: Hints for thin deep nets, arXiv preprint arXiv:1412.6550Google Search
13 
S. Park, J. Lee, H. Kim, 2024, Cosine similarity-guided knowledge distillation for robust object detectors, Scientific Reports, Vol. 14, No. 1, pp. 12345DOI
14 
I. Loshchilov, F. Hutter, 2017, SGDR: Stochastic gradient descent with warm restarts, arXiv preprint arXiv:1608.03983DOI
15 
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, 2004, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., Vol. 13, No. 4, pp. 600-612DOI
16 
P. Goyal, 2017, Accurate, large minibatch SGD: Training ImageNet in 1 hour, arXiv preprint arXiv:1706.02677DOI
17 
H.-J. Jung, D. Kim, S.-H. Na, K. Kim, 2023, Feature structure distillation with centered kernel alignment in BERT transferring, Expert Syst. Appl.DOI
18 
H. Huang, 2020, UNet 3+: A full-scale connected UNet for medical image segmentation, pp. 1055-1059DOI
19 
C.-B. Zhang, 2020, Delving deep into label smoothing, arXiv preprint arXiv:2011.12562DOI
20 
E. D. Gireesh, V. P. Gurupur, 2023, Information entropy measures for evaluation of reliability of deep neural network results, Entropy, Vol. 25, No. 4, pp. 573DOI
21 
H-G. Park, J-H. Bang, J-H. Kim, B-M. So, J-H. Song, K-M. Park, 2023, A Study on the Comparative Analysis of the Performance of CNN-Based Algorithms for the Determination of Arc Beads and Molten Mark by Model, The Journal of Next-generation Convergence Technology Association, Vol. 7, No. 4, pp. 543-552Google Search