  Convolutional neural networks (CNNs) have achieved great successes in many
computer vision problems. Unlike existing works that designed CNN architectures
to improve performance on a single task of a single domain and not
generalizable, we present IBN-Net, a novel convolutional architecture, which
remarkably enhances a CNN's modeling ability on one domain (e.g. Cityscapes) as
well as its generalization capacity on another domain (e.g. GTA5) without
finetuning. IBN-Net carefully integrates Instance Normalization (IN) and Batch
Normalization (BN) as building blocks, and can be wrapped into many advanced
deep networks to improve their performances. This work has three key
contributions. (1) By delving into IN and BN, we disclose that IN learns
features that are invariant to appearance changes, such as colors, styles, and
virtuality/reality, while BN is essential for preserving content related
information. (2) IBN-Net can be applied to many advanced deep architectures,
such as DenseNet, ResNet, ResNeXt, and SENet, and consistently improve their
performance without increasing computational cost. (3) When applying the
trained networks to new domains, e.g. from GTA5 to Cityscapes, IBN-Net achieves
comparable improvements as domain adaptation methods, even without using data
from the target domain. With IBN-Net, we won the 1st place on the WAD 2018
Challenge Drivable Area track, with an mIoU of 86.18%.
