Converging evidence suggests that the primate ventral visual pathway encodes increasingly
complex stimulus features in downstream areas. We quantitatively show that there indeed …

We explore a method for reconstructing visual stimuli from brain activity. Using large
databases of natural images we trained a deep convolutional generative adversarial network …

Recently, deep neural networks (DNNs) have been shown to provide accurate predictions
of neural responses across the ventral visual pathway. We here explore whether they also …

This paper reviews and discusses research advances on “explainable machine learning” in
computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic …

Research progress in computer vision and pattern recognition has led to a variety of modelling
techniques with (almost) human-like performance in a variety of tasks. A clear example of …

Encoding models are used for predicting brain activity in response to sensory stimuli with
the objective of elucidating how sensory information is represented in the brain. Encoding …

Abstract Representations learned by deep convolutional neural networks (CNNs) for object
recognition are a widely investigated model of the processing hierarchy in the human visual …

Personality analysis has been widely studied in psychology, neuropsychology, and signal
processing fields, among others. From the past few years, it also became an attractive …

Explainability and interpretability are two critical aspects of decision support systems. Despite
their importance, it is only recently that researchers are starting to explore these aspects. …

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 …