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Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
[article]
2020
arXiv
pre-print
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
arXiv:1807.09441v3
fatcat:qimj2laj7re6hlbb4v6lsjqgwu