Manifestation of Image Contrast in Deep Networks

TL;DR

This paper investigates how image contrast affects deep neural network performance, revealing that contrast augmentation improves invariance and accuracy, especially at low contrast, with minimal impact from architecture choice.

Contribution

It demonstrates that contrast augmentation during training enhances contrast invariance in deep networks and explores the influence of optimization and architecture on this effect.

Findings

Low-contrast images reduce network accuracy.

Contrast augmentation improves robustness to contrast variations.

First layers are more critical for contrast invariance.

Abstract

Contrast is subject to dramatic changes across the visual field, depending on the source of light and scene configurations. Hence, the human visual system has evolved to be more sensitive to contrast than absolute luminance. This feature is equally desired for machine vision: the ability to recognise patterns even when aspects of them are transformed due to variation in local and global contrast. In this work, we thoroughly investigate the impact of image contrast on prominent deep convolutional networks, both during the training and testing phase. The results of conducted experiments testify to an evident deterioration in the accuracy of all state-of-the-art networks at low-contrast images. We demonstrate that "contrast-augmentation" is a sufficient condition to endow a network with invariance to contrast. This practice shows no negative side effects, quite the contrary, it might allow a model to refrain from other illuminance related over-fittings. This ability can also be achieved by a short fine-tuning procedure, which opens new lines of investigation on mechanisms involved in two networks whose weights are over 99.9% correlated, yet astonishingly produce utterly different outcomes. Our further analysis suggests that the optimisation algorithm is an influential factor, however with a significantly lower effect; and while the choice of an architecture manifests a negligible impact on this phenomenon, the first layers appear to be more critical.