The reassembly kernels in CARAFE are generated conditioned on the low-resolution decoder feature map. The index maps predicted by IndexNet, however, build upon the high-resolution encoder feature map, before spatial information is lost;

3.  In IndexNet, each upsampled feature point only associates with a single point in the low-resolution feature map. From low resolution to high resolution, it is a one-to-many mapping. In CARAFE, each upsampled point is a weighted sum of a local region from the low-resolution feature map. This is a many-to-one mapping;

4.  Compared to CARAFE which is presented as a single upsampling operator, IndexNet is a more general framework.

In particular, the key difference lies in the intermediate path that allows spatial information to be visible to upsampling. To further demonstrate the benefit of this intermediate path, we present an image reconstruction experiment on synthetic data, namely, the Fashion-MNIST dataset [@xiao2017fashion].

Fashion-MNIST Image Reconstruction
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The idea is that, if an upsampling operator can recover spatial information well from downsampled feature maps, the reconstructed output should be visually closer to the input image. The quality of reconstruction results can be a good indicator how well spatial information is recovered by an upsampling operator.

*Network Architecture and 