论文标题
强大的深度学习，主动消除空间计算的主动噪声
Robust Deep Learning with Active Noise Cancellation for Spatial Computing
论文作者
论文摘要
本文提出了CANC是一种在空间计算中应用的共同教学的主动降噪方法，以解决具有极端嘈杂标签的深度学习。深度学习算法在土地或建筑足迹识别的空间计算方面取得了成功。但是，由于如何在空间计算和卫星图像中收集标签，因此在地面真相标签中存在很多噪音。现有的方法来处理极端标签的噪声进行清洁样品选择，并且不利用其余样品。由于数据检索的成本，此类技术可能会浪费。我们提出的CANC算法不仅可以保留高成本的训练样本，而且还提供了主动标签校正，可以通过极端嘈杂的标签更好地改善健壮的深度学习。我们证明了Canc在建立空间计算的占地面积识别方面的有效性。
This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning algorithms have been successful in spatial computing for land or building footprint recognition. However a lot of noise exists in ground truth labels due to how labels are collected in spatial computing and satellite imagery. Existing methods to deal with extreme label noise conduct clean sample selection and do not utilize the remaining samples. Such techniques can be wasteful due to the cost of data retrieval. Our proposed CANC algorithm not only conserves high-cost training samples but also provides active label correction to better improve robust deep learning with extreme noisy labels. We demonstrate the effectiveness of CANC for building footprint recognition for spatial computing.
