论文标题
卷积极端学习机的设计，用于基于视觉的小体导航
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies
论文作者
论文摘要
诸如卷积神经网络之类的深度学习体系结构是图像处理任务的计算机愿景的标准。然而，它们的准确性通常是以长期且计算昂贵的培训，对大型注释数据集的需求以及广泛的超参数搜索的成本。另一方面，一种称为卷积极端学习机的不同方法表明，训练时间急剧下降的潜力。太空图像，尤其是关于小物体的，很适合这种方法。在这项工作中，卷积极端学习机体系结构是针对其深层学习对应的设计和测试的。由于前者的训练时间相对较快，卷积极端学习机体系结构可以有效探索建筑设计空间，这对于后者来说是不切实际的，引入了一种用于计算机视觉任务的神经网络体系结构的方法。同样，研究图像处理方法与标记策略之间的耦合在考虑小体周围的基于视觉的导航时起着重要作用。
Deep learning architectures such as convolutional neural networks are the standard in computer vision for image processing tasks. Their accuracy however often comes at the cost of long and computationally expensive training, the need for large annotated datasets, and extensive hyper-parameter searches. On the other hand, a different method known as convolutional extreme learning machine has shown the potential to perform equally with a dramatic decrease in training time. Space imagery, especially about small bodies, could be well suited for this method. In this work, convolutional extreme learning machine architectures are designed and tested against their deep-learning counterparts. Because of the relatively fast training time of the former, convolutional extreme learning machine architectures enable efficient exploration of the architecture design space, which would have been impractical with the latter, introducing a methodology for an efficient design of a neural network architecture for computer vision tasks. Also, the coupling between the image processing method and labeling strategy is investigated and demonstrated to play a major role when considering vision-based navigation around small bodies.
