[20] Y. Zhang, T. Bu, J. Zhang, S. Tang, Z. Yu, J. K. Liu, and T. Huang, "Decoding pixel-level image features from two-photon calcium signals of macaque visual cortex," Neural Computation, vol. 34, pp. 1369-1397, 2022.
[21] T. Horikawa, A. S. Cowen, D. Keltner, and Y. Kamitani, “The neural representation of visually evoked emotion is high-dimensional, categorical, and distributed across transmodal brain regions,” iScience, vol. 23, 2019.
[22] A. G. Huth, T. Lee, S. Nishimoto, N. Y. Bilenko, A. T. Vu, and J. L. Gallant, "Decoding the semantic content of natural movies from human brain activity," Frontiers in Systems Neuroscience, vol. 10, 2016.
[23] T. Fang, Y. Qi, and G. Pan, "Reconstructing perceptive images from brain activity by shape-semantic gan," ArXiv, vol. abs/2101.12083, 2021.
[24] M. Ferrante, T. Boccato, and N. Toschi, "Semantic brain decoding: from fmri to conceptually similar image reconstruction of visual stimuli," arXiv preprint arXiv:2212.06726, 2022.
[25] F. Ozcelik and R. VanRullen, "Brain-diffuser: Natural scene reconstruction from fmri signals using generative latent diffusion," arXiv preprint arXiv:2303.05334, 2023.
[26] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851, 2020.
[27] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684–10695.
[28] G. Shen, K. Dwivedi, K. Majima, T. Horikawa, and Y. Kamitani, “End-to-end deep image reconstruction from human brain activity,” Frontiers in computational neuroscience, vol. 13, p. 21, 2019.
[29] G. Shen, T. Horikawa, K. Majima, and Y. Kamitani, "Deep image reconstruction from human brain activity," PLoS Computational Biology, vol. 15, 2017.
[30] X. Qian, Y. Wang, Y. Fu, X. Xue, and J. Feng, "Semantic neural decoding via cross-modal generation," arXiv preprint arXiv:2303.14730, 2023.
[31] S. Lin, T. Sprague, and A. K. Singh, "Mind reader: Reconstructing complex images from brain activities," arXiv preprint arXiv:2210.01769, 2022.
[32] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial networks," Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.
[33] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning. PMLR, 2015, pp. 2256–2265.
[34] A. Q. Nichol and P. Dhariwal, "Improved denoising diffusion probabilistic models," in International Conference on Machine Learning. PMLR, 2021, pp. 8162-8171.
[35] M. Li, T. Qu, W. Sun, and M.-F. Moens, "Alleviating exposure bias in diffusion models through sampling with shifted time steps," arXiv preprint arXiv:2305.15583, 2023.
[36] F. Bao, C. Li, J. Zhu, and B. Zhang, "Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models," arXiv preprint arXiv:2201.06503, 2022.
[37] P. Esser, R. Rombach, and B. Ommmer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 12873–12883.
[38] A. Van Den Oord, O. Vinyals et al., “Neural discrete representation learning,” Advances in neural information processing systems, vol. 30, 2017.
[39] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes et al., "Photorealistic text-to-image diffusion models with deep language understanding," arXiv preprint arXiv:2205.11487, 2022.
[40] G. Kim, T. Kwon, and J. C. Ye, "Diffusionclip: Text-guided diffusion models for robust image manipulation," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2426-2435.