  For visual object recognition tasks, the illumination variations can cause
distinct changes in object appearance and thus confuse the deep neural network
based recognition models. Especially for some rare illumination conditions,
collecting sufficient training samples could be time-consuming and expensive.
To solve this problem, in this paper we propose a novel neural network
architecture called Separating-Illumination Network (Sill-Net). Sill-Net learns
to separate illumination features from images, and then during training we
augment training samples with these separated illumination features in the
feature space. Experimental results demonstrate that our approach outperforms
current state-of-the-art methods in several object classification benchmarks.
