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iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling

by   Christian Etmann, et al.
University of Cambridge

U-Nets have been established as a standard neural network design architecture for image-to-image learning problems such as segmentation and inverse problems in imaging. For high-dimensional applications, as they for example appear in 3D medical imaging, U-Nets however have prohibitive memory requirements. Here, we present a new fully-invertible U-Net-based architecture called the iUNet, which allows for the application of highly memory-efficient backpropagation procedures. For this, we introduce learnable and invertible up- and downsampling operations. An open source library in Pytorch for 1D, 2D and 3D data is made available.


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Code Repositories


PyTorch Framework for Developing Memory Efficient Deep Invertible Networks

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A fully invertible U-Net for memory efficiency in Pytorch.

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