  Deep neural networks are ubiquitous due to the ease of developing models and
their influence on other domains. At the heart of this progress is
convolutional neural networks (CNNs) that are capable of learning
representations or features given a set of data. Making sense of such complex
models (i.e., millions of parameters and hundreds of layers) remains
challenging for developers as well as the end-users. This is partially due to
the lack of tools or interfaces capable of providing interpretability and
transparency. A growing body of literature, for example, class activation map
(CAM), focuses on making sense of what a model learns from the data or why it
behaves poorly in a given task. This paper builds on previous ideas to cope
with the increasing demand for interpretable, robust, and transparent models.
Our approach provides a simpler and intuitive (or familiar) way of generating
CAM. The proposed Eigen-CAM computes and visualizes the principle components of
the learned features/representations from the convolutional layers. Empirical
studies were performed to compare the Eigen-CAM with the state-of-the-art
methods (such as Grad-CAM, Grad-CAM++, CNN-fixations) by evaluating on
benchmark datasets such as weakly-supervised localization and localizing
objects in the presence of adversarial noise. Eigen-CAM was found to be robust
against classification errors made by fully connected layers in CNNs, does not
rely on the backpropagation of gradients, class relevance score, maximum
activation locations, or any other form of weighting features. In addition, it
works with all CNN models without the need to modify layers or retrain models.
Empirical results show up to 12% improvement over the best method among the
methods compared on weakly supervised object localization.
