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Reconstructing visual stimulus (image) only from human brain activitymeasured with functional Magnetic Resonance Imaging (fMRI) is a significant andmeaningful task in Human-AI collaboration. However, the inconsistentdistribution and representation between fMRI signals and visual images causethe heterogeneity gap. Moreover, the fMRI data is often extremelyhigh-dimensional and contains a lot of visually-irrelevant information.Existing methods generally suffer from these issues so that a satisfactoryreconstruction is still challenging. In this paper, we show that it is possibleto overcome these challenges by learning visually-guided cognitive latentrepresentations from the fMRI signals, and inversely decoding them to the imagestimuli. The resulting framework is called Dual-Variational Autoencoder/Generative Adversarial Network (D-VAE/GAN), which combines the advantages ofadversarial representation learning with knowledge distillation. In addition,we introduce a novel three-stage learning approach which enables the(cognitive) encoder to gradually distill useful knowledge from the paired(visual) encoder during the learning process. Extensive experimental results onboth artificial and natural images have demonstrated that our method couldachieve surprisingly good results and outperform all other alternatives.

Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning