[1] Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn. Fast, accurate, and lightweight super-resolution with cascading residual network. In ECCV, 2018. 3
[2] Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In BMVC, 2012. 6
[3] Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan. Cross aggregation transformer for image restoration. In NeurIPS, 2022. 1
[4] Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. Pact: Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085, 2018. 3
[5] Yoni Choukroun, Eli Kravchik, Fan Yang, and Pavel Kisilev. Low-bit quantization of neural networks for efficient inference. In ICCVW, 2019. 1, 3
[6] Xiangxiang Chu, Bo Zhang, Hailong Ma, Ruijun Xu, and Qingyuan Li. Fast, accurate and lightweight super-resolution with neural architecture search. arXiv preprint arXiv:1901.07261, 2019. 3
[7] Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Binarized neural networks: Training deep neural networks with weights and activations constrained to + 1 or-1. arXiv preprint arXiv:1602.02830, 2016. 2
[8] Yifu Ding, Haotong Qin, Qinghua Yan, Zhenhua Chai, Junjie Liu, Xiaolin Wei, and Xianglong Liu. Towards accurate post-training quantization for vision transformer. In ACM MM, 2022. 1, 3
[9] Zibin He, Tao Dai, Jian Lu, Yong Jiang, and Shu-Tao Xia. Fakd: Feature-affinity based knowledge distillation for efficient image super-resolution. In ICIP, 2020. 3
[10] Cheeun Hong, Sungyong Baik, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Cadyq: Content-aware dynamic quantization for image super-resolution. In ECCV, 2022. 2, 3, 6, 8, 9
[11] Cheeun Hong, Sungyong Baik, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Cadyq, 05 2023. 7
[12] Cheeun Hong, Heewon Kim, Sungyong Baik, Junghun Oh, and Kyoung Mu Lee. Daq: Channel-wise distribution-aware quantization for deep image super-resolution networks. In WACV, 2022. 3
[13] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, 2015. 6
[14] Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, and Daniel Soudry. Accurate post training quantization with small calibration sets. In ICML, 2021. 1, 3
[15] Zheng Hui, Xinbo Gao, Yunchu Yang, and Xiumei Wang. Lightweight image super-resolution with information multi-distillation network. In ACM MM, 2019. 3
[16] Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, et al. Efficient and accurate quantized image super-resolution on mobile npus, mobile ai & aim 2022 challenge: report. In ECCVW, 2022.3
[17] Divyansh J Hunjhunwala, Advait Gadhikar, Gauri Joshi, and Yonina C Eldar. Adaptive quantization of model updates for communication-efficient federated learning. In ICASSP, 2021. 3
[18] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Deeply-recursive convolutional network for image super-resolution. In CVPR, 2016. 1
[19] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 7
[20] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. Photo-realistic single image super-resolution using a generative adversarial network. In CVPR, 2017. 2, 7, 8, 9
[21] Wonkyung Lee, Junghyup Lee, Dohyung Kim, and Bumsub Ham. Learning with privileged information for efficient image super-resolution. In ECCV, 2020. 3
[22] Huixia Li, Chenqian Yan, Shaohui Lin, Xiawu Zheng, Baochang Zhang, Fan Yang, and Rongrong Ji. Pams: Quantized super-resolution via parameterized max scale. In ECCV, 2020. 2, 3, 6, 8, 9