 Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection . Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
<meaning-changed>  Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection . Furthermore, the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.
 Performance analysis shows that neural signals can be used to effectively supervise the training of deep learning models, as demonstrated by the achieved performance in both image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual manifold is consistent with cognitive neuroscience literature about visual perception and , most importantly, highlights new associations between brain areas, image patches and computational kernels.