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Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research.
J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini, “Distributed and overlapping representations of faces and objects in ventral temporal cortex,” Science , vol. 293, no. 5539, pp. 2425–2430, 2001
2001
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N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evolutionary Computation , vol. 9, no. 2, pp. 159–195, 2001
2001
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I. Levy, U. Hasson, G. Avidan, T. Hendler, and R. Malach, “Center–periphery organization of human object areas,” Nature neuroscience , vol. 4, no. 5, pp. 533–539, 2001
2001
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D. D. Cox and R. L. Savoy, “Functional magnetic resonance imaging (fmri)“brain reading”: detecting and classifying distributed patterns of fmri activity in human visual cortex,” Neuroimage , vol. 19, no. 2, pp. 261–270, 2003
2003
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
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L. X. Blonder, C. D. Smith, C. E. Davis, M. L. Kesler, T. F. Garrity, M. J. Avison, A. H. Andersen et al. , “Regional brain response to faces of humans and dogs,” Cognitive Brain Research , vol. 20, no. 3, pp. 384–394, 2004
2004
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Y. Kamitani and F. Tong, “Decoding the visual and subjective contents of the human brain,” Nature neuroscience , vol. 8, no. 5, pp. 679–685, 2005
2005
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J.-D. Haynes and G. Rees, “Predicting the orientation of invisible stimuli from activity in human primary visual cortex,” Nature neuroscience , vol. 8, no. 5, pp. 686–691, 2005
2005
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B. Thirion, E. Duchesnay, E. Hubbard, J. Dubois, J.-B. Poline, D. Lebihan, and S. Dehaene, “Inverse retinotopy: inferring the visual content of images from brain activation patterns,” Neuroimage , vol. 33, no. 4, pp. 1104–1116, 2006
2006
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K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant, “Identifying natural images from human brain activity,” Nature , vol. 452, no. 7185, pp. 352–355, 2008
2008
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Y. Miyawaki, H. Uchida, O. Yamashita, M.-a. Sato, Y. Morito, H. C. Tanabe, N. Sadato, and Y. Kamitani, “Visual image reconstruction from human brain activity using a combination of multiscale local image decoders,” Neuron , vol. 60, no. 5, pp. 915–929, 2008
2008
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
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S. Schoenmakers, M. Barth, T. Heskes, and M. Van Gerven, “Linear reconstruction of perceived images from human brain activity,” NeuroImage , vol. 83, pp. 951–961, 2013
2013
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2014
Cited alongside, same era.
2014
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
Cited alongside, same era.
A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=B1xsqj09Fm
2019
Later among the works it cites.
G. Gaziv, R. Beliy, N. Granot, A. Hoogi, F. Strappini, T. Golan, and M. Irani, “Self-supervised natural image reconstruction and rich semantic classification from brain activity,” bioRxiv , 2020
2020
Later among the works it cites.
M. Mozafari, L. Reddy, and R. VanRullen, “Reconstructing natural scenes from fmri patterns using bigbigan,” in 2020 International joint conference on neural networks (IJCNN) . IEEE, 2020, pp. 1–8
2020
Later among the works it cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8110–8119
2020
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T. Horikawa and Y. Kamitani, “Generic decoding of seen and imagined objects using hierarchical visual features,” Nature communications , vol. 8, no. 1, pp. 1–15, 2017
2017
Cited alongside, same era.
K. Seeliger, U. Güçlü, L. Ambrogioni, Y. Güçlütürk, and M. van Gerven, “Generative adversarial networks for reconstructing natural images from brain activity,” NeuroImage , vol. 181, pp. 775–785, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S105381191830658X
2018
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
Cited alongside, same era.
R. VanRullen and L. Reddy, “Reconstructing faces from fmri patterns using deep generative neural networks,” Communications biology , vol. 2, no. 1, pp. 1–10, 2019
2019
Cited alongside, same era.
G. Shen, T. Horikawa, K. Majima, and Y. Kamitani, “Deep image reconstruction from human brain activity,” PLoS computational biology , vol. 15, no. 1, p. e1006633, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Beliy, G. Gaziv, A. Hoogi, F. Strappini, T. Golan, and M. Irani, “From voxels to pixels and back: Self-supervision in natural-image reconstruction from fmri,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin, “Unsupervised learning of visual features by contrasting cluster assignments,” Advances in Neural Information Processing Systems , vol. 33, pp. 9912–9924, 2020
2020
Later among the works it cites.
A. Casanova, M. Careil, J. Verbeek, M. Drozdzal, and A. Romero, “Instance-conditioned GAN,” in Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., 2021. [Online]. Available: https://openreview.net/forum?id=aUuTEEcyY_
2021
Later among the works it cites.
A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A survey on contrastive self-supervised learning,” Technologies , vol. 9, no. 1, p. 2, 2021
2021
Later among the works it cites.
Z. Ren, J. Li, X. Xue, X. Li, F. Yang, Z. Jiao, and X. Gao, “Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning,” NeuroImage , vol. 228, p. 117602, 2021
2021
Later among the works it cites.
C. Zhuang, S. Yan, A. Nayebi, M. Schrimpf, M. C. Frank, J. J. DiCarlo, and D. L. Yamins, “Unsupervised neural network models of the ventral visual stream,” Proceedings of the National Academy of Sciences , vol. 118, no. 3, 2021
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763
2021
Later among the works it cites.
T. Konkle and G. A. Alvarez, “A self-supervised domain-general learning framework for human ventral stream representation,” Nature Communications , vol. 13, no. 1, pp. 1–12, 2022
2022
Closest in time.
Z. Gu, K. W. Jamison, M. Khosla, E. J. Allen, Y. Wu, T. Naselaris, K. Kay, M. R. Sabuncu, and A. Kuceyeski, “Neurogen: activation optimized image synthesis for discovery neuroscience,” NeuroImage , vol. 247, p. 118812, 2022
2022
Closest in time.