论文标题
IUNETS：完全可逆的U-Nets具有可学习的上下采样
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
论文作者
论文摘要
U-NET已被建立为图像到图像学习问题的标准体系结构，例如分割和成像中的逆问题。例如，对于大规模数据，例如，它出现在3D医学成像中，但是，U-NET具有超出的内存要求。在这里，我们提出了一种新的基于U-NET的新型架构，称为IUNET，该体系结构采用了新颖的可学习和可逆的上下采样操作，从而实现了记忆效率的反向传播。这使我们能够在相同的GPU内存限制下在实践中训练更深层，更大的网络。由于其可逆性，IUNET还可以用于构建归一化流。
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging. For large-scale data, as it for example appears in 3D medical imaging, the U-Net however has prohibitive memory requirements. Here, we present a new fully-invertible U-Net-based architecture called the iUNet, which employs novel learnable and invertible up- and downsampling operations, thereby making the use of memory-efficient backpropagation possible. This allows us to train deeper and larger networks in practice, under the same GPU memory restrictions. Due to its invertibility, the iUNet can furthermore be used for constructing normalizing flows.
