Abstract
Visual illusions teach us that what we see is not always
what is represented in the physical world. Their special nature make them a fascinating tool to test and validate any
new vision model proposed. In general, current vision models are based on the concatenation of linear and non-linear
operations. The similarity of this structure with the operations present in Convolutional Neural Networks (CNNs)
has motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions. In particular, we
show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the
extent of this replication varies with respect to variation in
architecture and spatial pattern size. These results suggest
that in order to obtain CNNs that better replicate human
behaviour, we may need to start aiming for them to better
replicate visual illusions.