Understanding pytorchs deeplabV3

The paper explains the interpolation strategy as well as the usage of transposed convolutions in a couple of sections.
This section might be interesting:

We have adopted instead a hybrid approach that strikes a good efficiency/accuracy trade-off, using atrous convolution to increase by a factor of 4 the density of computed feature maps, followed by fast bilinear interpolation by an additional factor of 8 to recover feature maps at the original image resolution. Bilinear interpolation is sufficient in this setting because the class score maps (corresponding to log-probabilities) are quite smooth, as illustrated in Fig. 5.

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