Introducing an Auxiliary Information Module into ANN for Distributional Change Adaptation

Author:

Yousef QaisORCID,Li PuORCID

Publisher

Springer Nature Switzerland

Reference33 articles.

1. Fiksel, J., Datta, A., Amouzou, A., Zeger, S.: Generalized Bayes quantification learning under dataset shift. J. Am. Stat. Assoc. 117(540), 2163–2181 (2022). https://doi.org/10.1080/01621459.2021.1909599

2. Liu, X., Li, Y., Meng, Q., Chen, G.: Deep transfer learning for conditional shift in regression. Knowl.-Based Syst..-Based Syst. 227, 107216 (2021). https://doi.org/10.1016/j.knosys.2021.107216

3. Jia, S., Deng, Y., Lv, J., Shichang, D., Xie, Z.: Joint distribution adaptation with diverse feature aggregation: a new transfer learning framework for bearing diagnosis across different machines. Measurement 187, 110332 (2022). https://doi.org/10.1016/j.measurement.2021.110332

4. Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A.A., Hardt, M.: Test-Time Training with Self-Supervision for Generalization under Distribution Shifts (2019)

5. Bartler, A., Bühler, A., Wiewel, F., Döbler, M., Yang, B.: MT3: meta test-time training for self-supervised test-time adaption. In: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, pp. 3080–3090 (2022). [Online]. Available: https://proceedings.mlr.press/v151/bartler22a.html

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