Convolutional Neural Networks Deceived by Visual Illusions

TL;DR

This study investigates whether CNNs trained on low-level visual tasks are deceived by visual illusions, revealing that they can replicate human responses and suggesting a link between perception and neural network behavior.

Contribution

The paper demonstrates that CNNs trained for denoising, deblurring, and color constancy can mimic human responses to visual illusions, highlighting a new connection between CNNs and human perception.

Findings

CNNs replicate human responses to visual illusions

Behavior varies with architecture and pattern size

Training for low-level tasks influences illusion susceptibility

Abstract

Visual illusions teach us that what we see is not always what it is represented in the physical world. Its 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 convolutions and non-linear operations. In this paper we get inspiration from the similarity of this structure with the operations present in Convolutional Neural Networks (CNNs). This 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. We believe that this CNNs behaviour appears as a by-product of the training for the low level vision tasks of denoising, color constancy or deblurring. Our work opens a new bridge between human perception and CNNs: in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions.