The researchers said existing deep learning based image inpainting methods suffer because the outputs for missing pixels necessarily depend on the value of the input that must be supplied to the neural network for those missing pixels. This leads to artifacts such as color discrepancy and blurriness in the images. To fix this problem, the NVIDIA team developed a method that guarantees the output for missing pixels does not depend on the input value supplied for those pixels. This method uses a "partial convolution" layer that renormalizes each output depending on the validity of its corresponding receptive field. This renormalization ensures that the value of the output is independent of the values of the missing pixels in each receptive field.