A perturbation-based metric using FGSM flips of ±1/255 instead of zero-masking gives more consistent and monotonic evaluation of attribution maps across 15 CNN-dataset pairs, with SmoothGrad ranked first.
Poly-CAM: High resolution class activation map for convolutional neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
The need for Explainable AI is increasing with the development of deep learning. The saliency maps derived from convolutional neural networks generally fail in localizing with accuracy the image features justifying the network prediction. This is because those maps are either low-resolution as for CAM [Zhou et al., 2016], or smooth as for perturbation-based methods [Zeiler and Fergus, 2014], or do correspond to a large number of widespread peaky spots as for gradient-based approaches [Sundararajan et al., 2017, Smilkov et al., 2017]. In contrast, our work proposes to combine the information from earlier network layers with the one from later layers to produce a high resolution Class Activation Map that is competitive with the previous art in term of insertion-deletion faithfulness metrics, while outperforming it in term of precision of class-specific features localization.
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach
A perturbation-based metric using FGSM flips of ±1/255 instead of zero-masking gives more consistent and monotonic evaluation of attribution maps across 15 CNN-dataset pairs, with SmoothGrad ranked first.