[48] S. Narang, G. F. Diamos, S. Sengupta, and E. Olsen. Exploring sparsity in recurrent neural networks. CoRR, abs/1704.05119, 2017. URL http://arxiv.org/abs/1704.05119.
[49] E. Perez, F. Strub, H. de Vries, V. Dumoulin, and A. Courville. Film: Visual reasoning with a general conditioning layer. Proceedings of the AAAI Conference on Artificial Intelligence, 32 (1), Apr. 2018.
[50] E. Plaut. From Principal Subspaces to Principal Components with Linear Autoencoders, Dec. 2018.
[51] B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. DoImagenet classifiers generalize toImagenet?, 2019.
[52] J. Rissanen and G. Langdon. Universal modeling and coding. IEEE Transactions on Information Theory, 27(1):12-23, 1981. doi: 10.1109/TIT.1981.1056282.
[53] A. Skodras, C. Christopoulos, and T. Ebrahimi. The jpeg 2000 still image compression standard. IEEE Signal Processing Magazine, 18(5):36-58, 2001. doi: 10.1109/79.952804.
[54] Y. Strümpler, J. Postels, R. Yang, L. van Gool, and F. Tombari. Implicit neural representations for image compression, 2022.
[55] H. Sun, L. Yu, and J. Katto. End-to-end learned image compression with quantized weights and activations, 2021. URL https://arxiv.org/abs/2111.09348.
[56] J. Sun, A. Mehra, B. Kailkhura, P.-Y. Chen, D. Hendrycks, J. Hamm, and Z. M. Mao. A spectral view of randomized smoothing under common corruptions: Benchmarking and improving certified robustness. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IV, pages 654-671. Springer, 2022.
[57] J. Sun, Q. Zhang, B. Kailkhura, Z. Yu, C. Xiao, and Z. M. Mao. Benchmarking robustness of 3d point cloud recognition against common corruptions. arXiv preprint arXiv:2201.12296, 2022.
[58] L. Theis, W. Shi, A. Cunningham, and F. Huszár. Lossy image compression with compressive autoencoders, 2017. URL https://arxiv.org/abs/1703.00395.
[59] G. Toderici, W. Shi, R. Timofte, L. Theis, J. Balle, E. Agustsson, N. Johnston, and F. Mentzer. Workshop and challenge on learned image compression (clic2020), 2020. URL http://www.compression.cc.
[60] G. Wallace. The jpeg still picture compression standard. IEEE Transactions on Consumer Electronics, 38(1):xviii-xxxiv, 1992. doi: 10.1109/30.125072.
[61] Z. Wang and A. C. Bovik. Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE signal processing magazine, 26(1):98-117, 2009.
[62] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. doi: 10.1109/TIP.2003.819861.
[63] 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.
[64] A. Wieckowski, J. Brandenburg, T. Hinz, C. Bartnik, V. George, G. Hege, C. Helmrich, A. Henkel, C. Lehmann, C. Stoffers, I. Zupancic, B. Bross, and D. Marpe. Vvenc: An open and optimized vvc encoder implementation. In Proc. IEEE International Conference on Multimedia Expo Workshops (ICMEW), pages 1-2. doi: 10.1109/ICMEW53276.2021.9455944.
[65] F. Yang, L. Herranz, Y. Cheng, and M. G. Mozerov. Slimmable compressive autoencoders for practical neural image compression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4998-5007, June 2021.
[66] D. Yin, R. G. Lopes, J. Shlens, E. D. Cubuk, and J. Gilmer. A Fourier Perspective on Model Robustness in Computer Vision. Curran Associates Inc., Red Hook, NY, USA, 2019.