C)\times4$

  : A Comparison of Model Complexity of Different Index Networks[]{data-label="tab:complexity_comparison"}

L: Linear; NL: Nonlinear; C: Context.

Guided Upsampling or Blind Upsampling: A Reconstruction-Based Justification
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Here we introduce the concept of *guided upsampling* and *blind upsampling* to summarize existing upsampling operators. Particularly, here we present a comparison between two data-dependent upsampling operators—IndexNet and CARAFE [@jiaqi2019carafe]. In addition, we show results of an image reconstruction task on synthetic data, highlighting the difference between guided upsampling and blind upsampling.

Guided Upsampling vs.  Blind Upsampling
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By blind upsampling, we mean that an upsampling operator that is pre-defined with fixed parameters. Guided upsampling, instead, is guided with the involvement of auxiliary information. Thus, most widely-used upsampling operators peform blind upsampling. These operators include nearest-neighbor (NN) interpolation, bilinear interpolation, space-to-depth and deconvolution. It is worth noting that the recent data-dependent upsampling operator CARAFE is also a blind upsampling operator. By contrast, guided upsampling operators are rare in literature. Max unpooling, albeit being simple, is a guided upsampling operator. The auxiliary information used in upsampling comes from the max-pooling indices. Thefore, our proposed IndexNet clearly implements guided upsampling, with inferred dynamic indices as the auxiliary information.

A taxonomy of commonly-used upsampling operators and their 