  In this paper, we analyze if cascade usage of the context encoder with
increasing input can improve the results of the inpainting. For this purpose,
we train context encoder for 64x64 pixels images in a standard way and use its
resized output to fill in the missing input region of the 128x128 context
encoder, both in training and evaluation phase. As the result, the inpainting
is visibly more plausible. In order to thoroughly verify the results, we
introduce normalized squared-distortion, a measure for quantitative inpainting
evaluation, and we provide its mathematical explanation. This is the first
attempt to formalize the inpainting measure, which is based on the properties
of latent feature representation, instead of L2 reconstruction loss.
