  Class Activation Mapping (CAM) has been widely adopted to generate saliency
maps which provides visual explanations for deep neural networks (DNNs). The
saliency maps are conventionally generated by fusing the channels of the target
feature map using a weighted average scheme. It is a weak model for the
inter-channel relation, in the sense that it only models the relation among
channels in a contrastive way (i.e., channels that play key roles in the
prediction are given higher weights for them to stand out in the fusion). The
collaborative relation, which makes the channels work together to provide cross
reference, has been ignored. Furthermore, the model has neglected the
intra-channel relation thoroughly.In this paper, we address this problem by
introducing Conceptor learning into CAM generation. Conceptor leaning has been
originally proposed to model the patterns of state changes in recurrent neural
networks (RNNs). By relaxing the dependency of Conceptor learning to RNNs, we
make Conceptor-CAM not only generalizable to more DNN architectures but also
able to learn both the inter- and intra-channel relations for better saliency
map generation. Moreover, we have enabled the use of Boolean operations to
combine the positive and pseudo-negative evidences, which has made the CAM
inference more robust and comprehensive. The effectiveness of Conceptor-CAM has
been validated with both formal verifications and experiments on the dataset of
the largest scale in literature. The experimental results show that
Conceptor-CAM is compatible with and can bring significant improvement to all
well recognized CAM-based methods, and has outperformed the state-of-the-art
methods by 43.14%~72.79% (88.39%~168.15%) on ILSVRC2012 in Average Increase
(Drop), 15.42%~42.55% (47.09%~372.09%) on VOC, and 17.43%~31.32%
(47.54%~206.45%) on COCO, respectively.
