DEEPCEPTION · Visual perception in deep neural networks

Visual perception in deep neural networks

Imagine you could regulate your emotions, thoughts, and perceptions as you please. Instead of listening to music, consuming alcohol or drugs, you would be able to click a button and a short while later a bad day would become the best day ever, scattered thoughts would give way and you would feel more focused than ever, ready to study for an exam. While such possibilities are still a far beyond our reach, in order to be able to do that one day, we need models of human brain. In other words, we need models that, given a particular stimulus, would produce an output indistinguishable from human response to that stimulus. Then, knowing the mapping, we could use this model to inform us what kind of stimulation would be optimal to elicit a desired mental state. In this project, we took the first steps in this direction by building accurate predictive models of human and non-human primate neural and behavioral responses in a demanding visual object recognition task. We focused on three major objectives: (i) establish an extensive benchmark of human visual processing; (ii) using this benchmark, evaluate the quality of machine decisions in relation to human performance; and (iii) using the insights gained from such a comparison, develop new, biologically-informed state-of-the art architectures. We successfully reached these goals, building a large-scale integrative bechmarking platform called Brain-Score, evaluating tens of models on it, and developing CORnet, the current best model of visual system. Going forward, we expect our heavily quantitative and engineering-focused approach to understanding visual system to scale to building the models of the entire brain.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз