论文标题
抵消均衡网络及其应用
Offset equivariant networks and their applications
论文作者
论文摘要
在本文中，我们介绍了一个框架，用于设计和实施偏移量比网络，即在输入中保留其输出统一增量的神经网络。在合适的彩色空间中，这种网络可实现表征在照明条件下变化的光度变换的肩variane。我们在三个不同的问题上验证了框架：图像识别，照明估计和图像插入。我们的实验表明，在常规数据上，偏移量比网络的性能与最新情况的表现相当。但是，当照明物的颜色变化时，与常规网络不同，e象网络的行为始终如一。
In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input. In a suitable color space this kind of networks achieves equivariance with respect to the photometric transformations that characterize changes in the lighting conditions. We verified the framework on three different problems: image recognition, illuminant estimation, and image inpainting. Our experiments show that the performance of offset equivariant networks are comparable to those in the state of the art on regular data. Differently from conventional networks, however, equivariant networks do behave consistently well when the color of the illuminant changes.
