  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.
