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Reconstructing visual stimuli from measured functional magnetic resonance imaging (fMRI) has been a meaningful and challenging task.
The solution-diffusion model: a review
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Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features
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Natural image reconstruction from fMRI using deep learning: A survey
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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
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Masked autoencoders are scalable vision learners
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Reconstruction of perceived images from fMRI patterns and semantic brain exploration using instance-conditioned GANs
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Decoding natural image stimuli from fMRI data with a surface-based convolutional network
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A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence
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