  Weakly supervised semantic segmentation produces pixel-level localization
from class labels; however, a classifier trained on such labels is likely to
focus on a small discriminative region of the target object. We interpret this
phenomenon using the information bottleneck principle: the final layer of a
deep neural network, activated by the sigmoid or softmax activation functions,
causes an information bottleneck, and as a result, only a subset of the
task-relevant information is passed on to the output. We first support this
argument through a simulated toy experiment and then propose a method to reduce
the information bottleneck by removing the last activation function. In
addition, we introduce a new pooling method that further encourages the
transmission of information from non-discriminative regions to the
classification. Our experimental evaluations demonstrate that this simple
modification significantly improves the quality of localization maps on both
the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new
state-of-the-art performance for weakly supervised semantic segmentation. The
code is available at: https://github.com/jbeomlee93/RIB.
