Z. Ji and M. Telgarsky. Directional convergence and alignment in deep learning. Advances in Neural Information Processing Systems, 33:17176-17186, 2020.
A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
N. Kumari, B. Zhang, S.-Y. Wang, E. Shechtman, R. Zhang, and J.-Y. Zhu. Ablating concepts in text-to-image diffusion models. Arxiv, 2023.
Y. LeCun, C. Cortes, and C. Burges. Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2, 2010.
N. Loo, R. Hasani, M. Lechner, and D. Rus. Dataset distillation fixes dataset reconstruction attacks. arXiv preprint arXiv:2302.01428, 2023.
K. Lyu and J. Li. Gradient descent maximizes the margin of homogeneous neural networks. arXiv preprint arXiv:1906.05890, 2019.
A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5188-5196, 2015.
A. Mordvintsev, C. Olah, and M. Tyka. Inceptionism: Going deeper into neural networks. 2015.
E. Moroshko, B. E. Woodworth, S. Gunasekar, J. D. Lee, N. Srebro, and D. Soudry. Implicit bias in deep linear classification: Initialization scale vs training accuracy. Advances in neural information processing systems, 33:22182-22193, 2020.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. 2011.
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Advances in neural information processing systems, 29, 2016.
A. Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski. Plug & play generative networks: Conditional iterative generation of images in latent space. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4467-4477, 2017.
A. Radhakrishnan, K. Yang, M. Belkin, and C. Uhler. Memorization in overparameterized autoencoders. arXiv preprint arXiv:1810.10333, 2018.
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein. Diffusion art or digital forgery? investigating data replication in diffusion models. arXiv preprint arXiv:2212.03860, 2022.
D. Soudry, E. Hoffer, M. S. Nacson, S. Gunasekar, and N. Srebro. The implicit bias of gradient descent on separable data. The Journal of Machine Learning Research, 19(1):2822-2878, 2018.
G. Vardi and O. Shamir. Implicit regularization in relu networks with the square loss. In Conference on Learning Theory, pages 4224-4258. PMLR, 2021.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004.
Z. Wang, J. Lee, and Q. Lei. Reconstructing training data from model gradient, provably. In International Conference on Artificial Intelligence and Statistics, pages 6595-6612. PMLR, 2023.
Y. Wen, J. Geiping, L. Fowl, M. Goldblum, and T. Goldstein. Fishing for user data in large-batch federated learning via gradient magnification. arXiv preprint arXiv:2202.00580, 2022.
Z. Yang, J. Zhang, E.-C. Chang, and Z. Liang. Neural network inversion in adversarial setting via background knowledge alignment. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 225-240, 2019.
H. Yin, P. Molchanov, J. M. Alvarez, Z. Li, A. Mallya, D. Hoiem, N. K. Jha, and J. Kautz. Dreaming to distill: Data-free knowledge transfer via deepinversion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8715-8724, 2020.