Open Access
| Issue |
ITM Web Conf.
Volume 80, 2025
2025 2nd International Conference on Advanced Computer Applications and Artificial Intelligence (ACAAI 2025)
|
|
|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 10 | |
| Section | Machine Learning & Deep Learning Algorithms | |
| DOI | https://doi.org/10.1051/itmconf/20258001020 | |
| Published online | 16 December 2025 | |
- T. M. C. Abbott, F. B. Abdalla, S. Allam et al., The Dark Energy Survey: Data Release 1. Astrophys. J. Suppl. Ser. 239, 18 (2018) [Google Scholar]
- A. Ćiprijanović, D. Kafkes, G. Snyder et al., DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. Mach. Learn.: Sci. Technol. 3, 035007 (2022) [Google Scholar]
- M. Walmsley, M. Bowles, A. M. M. Scaife et al., Scaling Laws for Galaxy Images. arXiv:2404.02973v1 [cs.CV] (2024) [Google Scholar]
- B. Aussel, S. Kruk, M. Walmsley et al., Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning. Astron. Astrophys. 689, A274 (2024). [Google Scholar]
- D. Doshi, JE. Kim, ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation. arXiv:2404.09886v1 [cs.LG](2024) [Google Scholar]
- E. Fielding, C. N. Nyirenda, M. Vaccari, The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques. In: 2022 Int. Conf. Electr. Comput. Energy Technol. (ICECET), Prague, pp. 1–6 (2022) [Google Scholar]
- S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: Proc. 32nd Int. Conf. Mach. Learn. (ICML), Lille, pp. 448–456 (2015) [Google Scholar]
- R. Yamashita, M. Nishio, R. K. G. Do et al., Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018) [CrossRef] [Google Scholar]
- S. Kalra, J. Wen, J. C. Cresswell et al., Decentralized federated learning through proxy model sharing. Nat. Commun. 14, 2899 (2023) [Google Scholar]
- N. Bhatt, N. Bhatt, P. Prajapati et al., A Data-Centric Approach to improve performance of deep learning models. Sci. Rep. 14, 22329 (2024) [Google Scholar]
- M. Walmsley, A. M. M. Scaife, C. Lintott et al., Practical galaxy morphology tools from deep supervised representation learning. Mon. Not. R. Astron. Soc. 513, 1581–1599 (2022) [Google Scholar]
- C. Gu, M. Lee, Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images. Bioengineering 11, 406 (2024) [Google Scholar]
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