%0 Generic %D 2017 %T Eccentricity Dependent Deep Neural Networks for Modeling Human Vision %A Gemma Roig %A Francis Chen %A X Boix %A Tomaso Poggio %B Vision Sciences Society %0 Conference Paper %B AAAI Spring Symposium Series, Science of Intelligence %D 2017 %T Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision %A Francis Chen %A Gemma Roig %A Leyla Isik %A X Boix %A Tomaso Poggio %X

Humans can recognize objects in a way that is invariant to scale, translation, and clutter. We use invariance theory as a conceptual basis, to computationally model this phenomenon. This theory discusses the role of eccentricity in human visual processing, and is a generalization of feedforward convolutional neural networks (CNNs). Our model explains some key psychophysical observations relating to invariant perception, while maintaining important similarities with biological neural architectures. To our knowledge, this work is the first to unify explanations of all three types of invariance, all while leveraging the power and neurological grounding of CNNs.

%B AAAI Spring Symposium Series, Science of Intelligence %G eng %U https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360