[65] Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, and Shu Kong. Long- tailed recognition via weight balancing. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6887-6897, 2022.
[66] Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shiliang Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, and Jun Zhu. One transformer fits all distributions in multi-modal diffusion at scale. ArXiv, abs/2303.06555, 2023.
[67] Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, and Jun Zhu. Diffusion models and semi-supervised learners benefit mutually with few labels. ArXiv, abs/2302.10586, 2023.
[68] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. ArXiv, abs/2206.00364, 2022.
[69] Diederik P. Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. ArXiv, abs/1807.03039, 2018.
[70] Xiulong Yang, Sheng-Min Shih, Yinlin Fu, Xiaoting Zhao, and Shihao Ji. Your vit is secretly a hybrid discriminative-generative diffusion model. arXiv preprint arXiv:2208.07791, 2022.
[71] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021.
[72] Wanshu Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, and Yu-Chiang Frank Wang. Frido: Feature pyramid diffusion for complex scene image synthesis. ArXiv, abs/2208.13753, 2022.
[73] Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[74] Dongjun Kim, Seung-Jae Shin, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon. Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation. In International Conference on Machine Learning, 2021.
[75] Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). arXiv: Learning, 2015.
[76] Abien Fred Agarap. Deep learning using rectified linear units (relu). ArXiv, abs/1803.08375, 2018.
[77] Senwei Liang, Zhongzhan Huang, Mingfu Liang, and Haizhao Yang. Instance enhancement batch normalization: an adaptive regulator of batch noise. In AAAI Conference on Artificial Intelligence, 2019.
[78] Diganta Misra. Mish: A self regularized non-monotonic activation function. In British Machine Vision Conference, 2020.
[79] Jie Hu, Li Shen, Samuel Albanie, Gang Sun, and Enhua Wu. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42:2011-2023, 2017.
[80] Shan Zhong, Wushao Wen, and Jinghui Qin. Switchable self-attention module. ArXiv, abs/2209.05680, 2022.
[81] Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In-So Kweon. Cbam: Convolutional block attention module. In European Conference on Computer Vision, 2018.
[82] Zhongzhan Huang, Senwei Liang, Mingfu Liang, and Haizhao Yang. Dianet: Dense-and-implicit attention network. In AAAI Conference on Artificial Intelligence, 2019.
[83] Gavin E Crooks. Survey of simple, continuous, univariate probability distributions. Technical report, Technical report, Lawrence Berkeley National Lab, 2013., 2012.
[84] Robert B Davies. Algorithm as 155: The distribution of a linear combination of  $\chi$  2 random variables. Applied Statistics, pages 323-333, 1980.
[85] Robert B Davies. Numerical inversion of a characteristic function. Biometrika, 60(2):415-417, 1973.
[86] Zhongzhan Huang, Wenqi Shao, Xinjiang Wang, Liang Lin, and Ping Luo. Rethinking the pruning criteria for convolutional neural network. Advances in Neural Information Processing Systems, 34:16305-16318, 2021.
[87] Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. A convergence theory for deep learning via overparameterization. In International Conference on Machine Learning, pages 242-252. PMLR, 2019.