[72] H. A. Just, F. Kang, J. T. Wang, Y. Zeng, M. Ko, M. Jin, and R. Jia, "Lava: Data valuation without pre-specified learning algorithms," arXiv preprint arXiv:2305.00054, 2023. 3
[73] A. Ilyas, S. M. Park, L. Engstrom, G. Leclerc, and A. Madry, "Datamodels: Predicting predictions from training data," arXiv preprint arXiv:2202.00622, 2022. 3
[74] C.-K. Yeh, J. Kim, I. E.-H. Yen, and P. K. Ravikumar, “Representer point selection for explaining deep neural networks,” Advances in neural information processing systems, vol. 31, 2018. 3
[75] Y. Song and S. Ermon, "Generative modeling by estimating gradients of the data distribution," Advances in neural information processing systems, vol. 32, 2019. 3
[76] Z. Deng, L. Zhang, K. Vodrahalli, K. Kawaguchi, and J. Y. Zou, “Adversarial training helps transfer learning via better representations,” Advances in Neural Information Processing Systems, vol. 34, pp. 25179-25191, 2021. 4
[77] H. Liu, S. M. Xie, Z. Li, and T. Ma, "Same pre-training loss, better downstream: Implicit bias matters for language models," arXiv preprint arXiv:2210.14199, 2022. 4
[78] H. Liu, M. Long, J. Wang, and M. I. Jordan, “Towards understanding the transferability of deep representations,” arXiv preprint arXiv:1909.12031, 2019. 4, 5, 6
[79] M. T. Rosenstein, Z. Marx, L. P. Kaelbling, and T. G. Dietterich, “To transfer or not to transfer,” in NIPS 2005 workshop on transfer learning, vol. 898, no. 3, 2005. 4
[80] L. Duan, D. Xu, and S.-F. Chang, “Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach,” in 2012 IEEE Conference on computer vision and pattern recognition. IEEE, 2012, pp. 1338-1345.
[81] L. Ge, J. Gao, H. Ngo, K. Li, and A. Zhang, "On handling negative transfer and imbalanced distributions in multiple source transfer learning," Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 7, no. 4, pp. 254-271, 2014.
[82] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" Advances in neural information processing systems, vol. 27, 2014.
[83] Z. Cao, M. Long, J. Wang, and M. I. Jordan, “Partial transfer learning with selective adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 2724–2732.
[84] Z. Wang, Z. Dai, B. Póczos, and J. Carbonell, “Characterizing and avoiding negative transfer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 11-293-11-302. 4
[85] D. Guo, A. M. Rush, and Y. Kim, “Parameter-efficient transfer learning with diff pruning,” arXiv preprint arXiv:2012.07463, 2020. 4
[86] B. Liu, Y. Cai, Y. Guo, and X. Chen, “Transtailor: Pruning the pre-trained model for improved transfer learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, 2021, pp. 8627–8634.
[87] T. Chen, J. Frankle, S. Chang, S. Liu, Y. Zhang, M. Carbin, and Z. Wang, “The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16306-16316. 7
[88] T. Chen, Y. Cheng, Z. Gan, L. Yuan, L. Zhang, and Z. Wang, “Chasing sparsity in vision transformers: An end-to-end exploration,” Advances in Neural Information Processing Systems, vol. 34, pp. 19 974–19 988, 2021.
[89] S. Myung, I. Huh, W. Jang, J. M. Choe, J. Ryu, D. Kim, K.-E. Kim, and C. Jeong, “Pac-net: A model pruning approach to inductive transfer learning,” in International Conference on Machine Learning. PMLR, 2022, pp. 16240-16252. 4