Empirical evaluation of GMM, GAN, and VAE models for synthesizing diverse task-dependent fMRI images shows they can augment classifiers with performance gains complementary to the choice of predictive model.
Deep learning for brain decoding,
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FMRI data augmentation via synthesis
Empirical evaluation of GMM, GAN, and VAE models for synthesizing diverse task-dependent fMRI images shows they can augment classifiers with performance gains complementary to the choice of predictive model.