[1] Kaiming He, Xiangyu Zhang, et al. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[2] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In ECCV, pages 21-37. Springer, 2016.
[3] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.
[4] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[5] Raghuraman Krishnamoorthi. Quantizing deep convolutional networks for efficient inference: A whitepaper. arXiv preprint arXiv:1806.08342, 2018.
[6] Matthieu Courbariaux, Itay Hubara, et al. Binarized neural networks: Training deep neural networks with weights and activations constrained to + 1 or -1. NeurIPS, 2016.
[7] Shuang Wu, Guoqi Li, Feng Chen, and Luping Shi. Training and inference with integers in deep neural networks. *ICLR*, 2018.
[8] Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In CVPR, pages 2704-2713, 2018.
[9] Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, and Tim Genewein. Variational network quantization. In ICLR, 2018.
[10] Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, and Max Welling. Relaxed quantization for discretized neural networks. *ICLR*, 2018.
[11] Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, and Mickey Aleksic. A quantization-friendly separable convolution for mobilenets. In 2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), pages 14-18. IEEE, 2018.
[12] Kanghyun Choi, Hye Yoon Lee, Deokki Hong, Joonsang Yu, Noseong Park, Youngsok Kim, and Jinho Lee. It's all in the teacher: Zero-shot quantization brought closer to the teacher. In CVPR, pages 8311-8321, 2022.
[13] Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, and Rongrong Ji. Intraq: Learning synthetic images with intra-class heterogeneity for zero-shot network quantization. In CVPR, pages 12339-12348, 2022.
[14] Markus Nagel, Mart van Baalen, et al. Data-free quantization through weight equalization and bias correction. In ICCV, pages 1325-1334, 2019.
[15] Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman. Same, same but different: Recovering neural network quantization error through weight factorization. In ICML, pages 4486-4495, 2019.
[16] Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Chris De Sa, and Zhiru Zhang. Improving neural network quantization without retraining using outlier channel splitting. In ICML, pages 7543-7552, 2019.
[17] Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W Mahoney, and Kurt Keutzer. Zeroq: A novel zero shot quantization framework. In CVPR, pages 13169-13178, 2020.
[18] Xiangguo Zhang, Haotong Qin, Yifu Ding, Ruihao Gong, Qinghua Yan, Renshuai Tao, Yuhang Li, Fengwei Yu, and Xianglong Liu. Diversifying sample generation for accurate data-free quantization. In CVPR, pages 15658-15667, 2021.
[19] Guo Cong et al. Squant: On-the-fly data-free quantization via diagonal hessian approximation. ICLR, 2022.
[20] Nvidia. Nvidia a100 tensor coregpu architecture. web tech report (https://images.nvidia.com/aem-dam/en-zz/Solutions/data-center/nvidia-ampere-architecture-whitepaper.pdf), 2021.
[21] Majid Rabbani. Jpeg2000: Image compression fundamentals, standards and practice. Journal of Electronic Imaging, 11(2):286, 2002.
[22] Stephane G Mallat. A theory for multiresolution signal decomposition: the wavelet representation. In Fundamental Papers in Wavelet Theory, pages 494-513. Princeton University Press, 2009.
[23] Karen Ullrich, Edward Meeds, and Max Welling. Soft weight-sharing for neural network compression. *ICLR*, 2017.