资源分类
》 资源 》论文》Reconstructing perceived faces from brain activations with deep adversarial neural decoding

Reconstructing perceived faces from brain activations with deep adversarial neural decoding

2020-02-10 | |  53 |   47 |   0

Abstract 

Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts the nonlinear transformation from perceived stimuli to latent features with adversarial training of convolutional neural networks. We test our approach with a functional magnetic resonance imaging experiment and show that it can generate state-of-the-art reconstructions of perceived faces from brain activations.

上一篇：Wasserstein Learning of Deep Generative Point Process Models

下一篇：ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

用户评价
全部评价

热门资源

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Rating-Boosted La...

    The performance of a recommendation system reli...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...