  Class activation mapping~(CAM), a visualization technique for interpreting
deep learning models, is now commonly used for weakly supervised semantic
segmentation~(WSSS) and object localization~(WSOL). It is the weighted
aggregation of the feature maps by activating the high class-relevance ones.
Current CAM methods achieve it relying on the training outcomes, such as
predicted scores~(forward information), gradients~(backward information), etc.
However, when with small-scale data, unstable training may lead to less
effective model outcomes and generate unreliable weights, finally resulting in
incorrect activation and noisy CAM seeds. In this paper, we propose an
outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly
supervised applications. Since broad learning system (BLS) is independent to
the model learning, BroadCAM can avoid the weights being affected by the
unreliable model outcomes when with small-scale data. By evaluating BroadCAM on
VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and
OpenImages30k for WSOL, BroadCAM demonstrates superior performance than
existing CAM methods with small-scale data (less than 5\%) in different CNN
architectures. It also achieves SOTA performance with large-scale training
data. Extensive qualitative comparisons are conducted to demonstrate how
BroadCAM activates the high class-relevance feature maps and generates reliable
CAMs when with small-scale training data.
