Deep neural networks addressing computer vision tasks commonly push the input visual **data** through a sequence of operations. A common trend of this sequential processing is that the input **data** is internally resampled until reaching the desired prediction space. As mentioned in Sec. 2, methods aiming at interpretation/explanation start from an internal point in the network and go backwards until reaching the input space -producing a heatmap. However, due to the resampling process, heatmaps generated by the backwards process tend to display grid-like artifacts. More precisely, we find that this grid effect is caused by the internal resampling introduced by network operations with stride larger than one (S>1). To alleviate this effect, in the backwards pass, we set the stride S=1 and compensate this change by modifying the input accordingly. As a result, the backwards process can be executed while maintaining the network structure.