Overview of Class Activation Maps for Visualization Explainability

Recent research in deep learning methodology has led to a variety of complex modelling techniques in computer vision (CV) that reach or even outperform human performance. Although these black-box deep learning models have obtained astounding results, they are limited in their interpretability and transparency which are critical to take learning machines to the next step to include them in sensitive decision-support systems involving human supervision. Hence, the development of explainable techniques for computer vision (XCV) has recently attracted increasing attention. In the realm of XCV, Class Activation Maps (CAMs) have become widely recognized and utilized for enhancing interpretability and insights into the decision-making process of deep learning models. This work presents a comprehensive overview of the evolution of Class Activation Map methods over time. It also explores the metrics used for evaluating CAMs and introduces auxiliary techniques to improve the saliency of these methods. The overview concludes by proposing potential avenues for future research in this evolving field.Comment: 6 page

Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball

We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leaving the background unaltered. As a semantically meaningful adverse perturbations, it forms a bridge between counterfactual explanations and adversarial perturbations in the space of images. We evaluate our approach on several standard explainability benchmarks, namely, weak localization, insertion deletion, and the pointing game demonstrating that perceptually regularized counterfactuals are an effective explanation for image-based classifiers.Comment: CVPR 202

On Saliency Maps and Adversarial Robustness

A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior can be quantified by considering the alignment between input image and saliency map. In this work, we provide a different perspective to this coupling, and provide a method, Saliency based Adversarial training (SAT), to use saliency maps to improve adversarial robustness of a model. In particular, we show that using annotations such as bounding boxes and segmentation masks, already provided with a dataset, as weak saliency maps, suffices to improve adversarial robustness with no additional effort to generate the perturbations themselves. Our empirical results on CIFAR-10, CIFAR-100, Tiny ImageNet and Flower-17 datasets consistently corroborate our claim, by showing improved adversarial robustness using our method. saliency maps. We also show how using finer and stronger saliency maps leads to more robust models, and how integrating SAT with existing adversarial training methods, further boosts performance of these existing methods.Comment: Accepted at ECML-PKDD 2020, Acknowledgements adde