corresponding downsampling operators is summarized in Table \[tab:upsampling\]. An upsampling operator should generally have an corresponding downsampling operator, and vice versa.

            Upsampling          Downsampling
  -- ------------------------ -----------------
         NN Interpolation      Average Pooling
      Bilinear Interpolation     Convolution
          Deconvolution          Convolution
          Space-to-Depth       Depth-to-Space
              CARAFE             Convolution
          Max Unpooling          Max Pooling
        Indexed Upsampling     Indexed Pooling

  : Blind Upsampling and Guided Upsampling Operators[]{data-label="tab:upsampling"}

The main difference here is that guided upsampling is made possible to exploit extra information to better recover the spatial information during upsampling. Thus, it is important that the spatial information is properly encoded during downsampling and is transferred to unsampling.

IndexNet vs.  CARAFE {#ssec:comparison}
--------------------

Both IndexNet and CARAFE are one of the few attempts pursuing the idea of data-dependent upsampling. The similarities include:

1.  They both are related to dynamic networks.

2.  Both are parametric upsampling operators;

3.  CARAFE and HINs both perform holistic upsampling.

4.  CARAFE also learns an index function. The index function has an identical form to Eq. , but with dynamic and normalized ${\mbox{\boldsymbol{$W$}}}$. In this sense, CARAFE may be considered as a single-input version of $\mathcal{IU}$, where the index map is generated internally.

The differences are:

1.  CARAFE is a blind upsampling operator, while IndexNet implements guided upsampling;

2.  