Jan 13, 2019 · In this paper, we propose a model, DCNN-GAN, by combining a reconstruction network and GAN. We utilize the CNN for hierarchical feature ...
This paper proposes a model, DCNN-GAN, by combining a reconstruction network and GAN, that outperforms previous works, regarding reconstruction quality and ...
Sep 6, 2024 · In this paper, we propose a model, DCNN-GAN, by combining a reconstruction network and GAN. We utilize the CNN for hierarchical feature ...
In the DCNN-. GAN, the reconstruction network outputs the coarse image from the decoded features. The GAN generates a more realistic image from the coarse one.
The GAN model is based on the pytorch implementation of pix2pix. The fMRI data is obtained using the datasets from Generic Object Decoding.
In this paper, we show that it is possible to obtain a promising solution by learning visually-guided latent cognitive representations from the fMRI signals.
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Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology.
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Dec 20, 2021 · In this work, we survey the most recent deep learning methods for natural image reconstruction from fMRI.
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Sep 20, 2023 · In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals.
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The TS-ML-DFM method proposed in this study, when applied to decoding brain visual patterns using fMRI data, has outperformed previous algorithms.