基于随机凸化和频域最小化的高效卷积自动编码
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
摘要：
深度卷积神经网络（CNN）等深度学习架构的无处不在是由不断增加的标记数据集和专用硬件的协同组合推动的。尽管取得了无可争议的成功，但在接近新应用时，对大量标记数据和专用硬件的依赖可能是一个限制因素。为了帮助缓解这些限制，我们提出了一种有效的学习策略，用于在可接受的时间内在传统硬件上对深度CNN进行分层无监督训练。我们提出的策略包括随机凸化重构压缩自动编码（RCAE）学习目标，并通过坐标下降（CD）在频域中解决由此产生的大规模凸最小化问题。我们提出的学习策略的主要优点是：（1）单个可调优化参数;（2）快速且有保证的收敛;（3）完全并行化的可能性。数值实验表明，我们提出的学习策略规模（在最坏的情况下）与图像大小，过滤器的数量和过滤器的大小呈线性关系。
英文摘要：
The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiting factor when approaching new applications. To help alleviating these limitations, we propose an efficient learning strategy for layer-wise unsupervised training of deep CNNs on conventional hardware in acceptable time. Our proposed strategy consists of randomly convexifying the reconstruction contractive auto-encoding (RCAE) learning objective and solving the resulting large-scale convex minimization problem in the frequency domain via coordinate descent (CD). The main advantages of our proposed learning strategy are: (1) single tunable optimization parameter; (2) fast and guaranteed convergence; (3) possibilities for full parallelization. Numerical experiments show that our proposed learning strategy scales (in the worst case) linearly with image size, number of filters and filter size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
