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Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity(More)
Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making(More)
ground truth sketch inverse sketch deep neural network Figure 1: Example results of our convolutional sketch inversion models. Our models invert face sketches to synthesize photorealistic face images. Each row shows the sketch inversion / photo synthesis pipeline that transforms a different sketch of the same face to a different image of the same face via a(More)
A brain computer interface (BCI) enables direct communication between a brain and a computer translating brain activity into computer commands usi ng preprocessing, feature extraction and classification operations. Feature extraction is crucial as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been(More)
Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial(More)
Better acquisition protocols and analysis techniques are making it possible to use fMRI to obtain highly detailed visualizations of brain processes. In particular we focus on the reconstruction of natural images from BOLD responses in visual cortex. We expand our linear Gaussian framework for percept decoding with Gaussian mixture models to better represent(More)
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