Sept. 2, 2023, 12:24 a.m. | /u/Christs_Elite

Machine Learning

Hi fellow computer scientists and engineers,

I've been wondering why do we often have a convolution inside every upsample and downsample block. Well, it makes sense, if you intend to upscale some features and use a bilinear interpolation, then some error can be introduced due to interpolation inaccuracies. This is where convolution layer comes handy to help and support the upscaling. But is this really the reason behind it? Or is there a deeper explanation?

Also, just for …

computer convolution engineers error features inside machinelearning scientists sense upscale

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