[19] Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. PACT: parameterized clipping activation for quantized neural networks. CoRR, abs/1805.06085, 2018.
[20] Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, and Nojun Kwak.  $\mathrm{LSQ + }$  improving low-bit quantization through learnable offsets and better initialization. Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[21] Eunhyeok Park and Sungjoo Yoo. PROFIT: A novel training method for sub-4-bit mobilenet models. European Conference on Computer Vision (ECCV), 2020.
[22] Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling. Data-free quantization through weight equalization and bias correction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1325-1334, 2019.
[23] Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. Up or down? adaptive rounding for post-training quantization. In International Conference on Machine Learning, pages 7197-7206. PMLR, 2020.
[24] Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Brecq: Pushing the limit of post-training quantization by block reconstruction. In International Conference on Learning Representations, 2021.
[25] Xiuying Wei, Ruihao Gong, Yuhang Li, Xianglong Liu, and Fengwei Yu. Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization. In International Conference on Learning Representations, 2022.
[26] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[27] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022.
[28] Sangil Jung, Changyong Son, Seohyung Lee, JinWoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi. Learning to quantize deep networks by optimizing quantization intervals with task loss. Conference on Computer Vision and Pattern Recognition, (CVPR), 2019.
[29] Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha. Learned step size quantization. International Conference on Learning Representations (ICLR), 2020.
[30] Juncheol Shin, Junhyuk So, Sein Park, Seungyeop Kang, Sungjoo Yoo, and Eunhyeok Park. Nipq: Noise proxy-based integrated pseudo-quantization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3852–3861, June 2023.
[31] Yunshan Zhong, Mingbao Lin, Xunchao Li, Ke Li, Yunhang Shen, Fei Chao, Yongjian Wu, and Rongrong Ji. Dynamic dual trainable bounds for ultra-low precision super-resolution networks. arXiv preprint arXiv:2203.03844, 2022.
[32] Changhun Lee, Hyungjun Kim, Eunhyeok Park, and Jae-Joon Kim. Insta-bnn: Binary neural network with instance-aware threshold. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 17325-17334, 2023.
[33] Cheeun Hong, Sungyong Baik, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Cadyq: Content-aware dynamic quantization for image super-resolution. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part VII, pages 367-383. Springer, 2022.