论文标题
深层结构学习在训练有素的投影空间中使用特征提取
Deep Structure Learning using Feature Extraction in Trained Projection Space
论文作者
论文摘要
在机器学习的最后十年中，卷积神经网络一直是提取丰富的感觉和高维数据的最惊人的成功。尽管通过卷积学习的数据表示形式已经在各种深度学习库中进行了充分的研究，有效地实现，但人们通常会面临有限的记忆力和数量不足的培训数据，尤其是对于高度和大规模的任务。为了克服这些局限性，我们使用ra-transform的自调整和数据依赖版本（也称为X射线投影）引入网络体系结构，以通过低维空间中的卷积来提取特征。所得的框架（名为Pinet）可以端对端训练，并在体积分段任务上显示出令人鼓舞的性能。我们在公共数据集上测试提出的模型，以表明我们的方法仅使用分数参数才能获得可比的结果。与其他分割模型相比，对记忆使用和处理时间的研究证实了Pinet的提高效率。
Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data. While learning data representations via convolutions is already well studied and efficiently implemented in various deep learning libraries, one often faces limited memory capacity and insufficient number of training data, especially for high-dimensional and large-scale tasks. To overcome these limitations, we introduce a network architecture using a self-adjusting and data dependent version of the Radon-transform (linear data projection), also known as x-ray projection, to enable feature extraction via convolutions in lower-dimensional space. The resulting framework, named PiNet, can be trained end-to-end and shows promising performance on volumetric segmentation tasks. We test proposed model on public datasets to show that our approach achieves comparable results only using fractional amount of parameters. Investigation of memory usage and processing time confirms PiNet's superior efficiency compared to other segmentation models.
