论文标题
宽带蝴蝶网络：通过多频神经网络稳定而有效的反转
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
论文作者
论文摘要
我们介绍了一个称为宽带蝴蝶网络（宽键）的端到端深度学习架构，用于近似宽带散射数据的反向散射图。该体系结构结合了计算谐波分析（例如蝴蝶分解）和传统多尺度方法（例如Cooley-Tukey FFT算法）的工具，以大大减少可训练参数的数量，以匹配问题的固有复杂性。结果，宽字节是有效的：它所需的训练点要比现成的架构更少，并且具有稳定的训练动态，因此它可以依靠标准的重量初始化策略。该体系结构自动适应了数据的尺寸，仅使用用户必须指定的几个超参数。 Widebnet能够产生具有基于优化的方法竞争的图像，但要以成本的一小部分，我们还从数字上证明了它在完整的孔径散射设置中学会了超级溶解的散射器。
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data. This architecture incorporates tools from computational harmonic analysis, such as the butterfly factorization, and traditional multi-scale methods, such as the Cooley-Tukey FFT algorithm, to drastically reduce the number of trainable parameters to match the inherent complexity of the problem. As a result WideBNet is efficient: it requires fewer training points than off-the-shelf architectures, and has stable training dynamics, thus it can rely on standard weight initialization strategies. The architecture automatically adapts to the dimensions of the data with only a few hyper-parameters that the user must specify. WideBNet is able to produce images that are competitive with optimization-based approaches, but at a fraction of the cost, and we also demonstrate numerically that it learns to super-resolve scatterers in the full aperture scattering setup.
