  To minimize the annotation costs associated with the training of semantic
segmentation models, researchers have extensively investigated
weakly-supervised segmentation approaches. In the current weakly-supervised
segmentation methods, the most widely adopted approach is based on
visualization. However, the visualization results are not generally equal to
semantic segmentation. Therefore, to perform accurate semantic segmentation
under the weakly supervised condition, it is necessary to consider the mapping
functions that convert the visualization results into semantic segmentation.
For such mapping functions, the conditional random field and iterative
re-training using the outputs of a segmentation model are usually used.
However, these methods do not always guarantee improvements in accuracy;
therefore, if we apply these mapping functions iteratively multiple times,
eventually the accuracy will not improve or will decrease.
  In this paper, to make the most of such mapping functions, we assume that the
results of the mapping function include noise, and we improve the accuracy by
removing noise. To achieve our aim, we propose the self-supervised difference
detection module, which estimates noise from the results of the mapping
functions by predicting the difference between the segmentation masks before
and after the mapping. We verified the effectiveness of the proposed method by
performing experiments on the PASCAL Visual Object Classes 2012 dataset, and we
achieved 64.9\% in the val set and 65.5\% in the test set. Both of the results
become new state-of-the-art under the same setting of weakly supervised
semantic segmentation.
