Proceedings ArticleDOI
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
Saurabh Desai,Harish G. Ramaswamy +1 more
- pp 983-991
TLDR
This approach – Ablation-based Class Activation Mapping (Ablation CAM) uses ablation analysis to determine the importance of individual feature map units w.r.t. class to produce a coarse localization map highlighting the important regions in the image for predicting the concept.Abstract:
In response to recent criticism of gradient-based visualization techniques, we propose a new methodology to generate visual explanations for deep Convolutional Neural Networks (CNN) - based models. Our approach – Ablation-based Class Activation Mapping (Ablation CAM) uses ablation analysis to determine the importance (weights) of individual feature map units w.r.t. class. Further, this is used to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Our objective and subjective evaluations show that this gradient-free approach works better than state-of-the-art Grad-CAM technique. Moreover, further experiments are carried out to show that Ablation-CAM is class discriminative as well as can be used to evaluate trust in a model.read more
Citations
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Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study
TL;DR: Four key challenges are identi-fied for the application of deep WSOL methods in histology – under/over activation of CAMs, sensitivity to thresholding, and model selection – and research avenues are provided to mitigate these issues.
Journal ArticleDOI
Disassembling Convolutional Segmentation Network
Journal ArticleDOI
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
TL;DR: Avatar Knowledge Distillation (AKD) as discussed by the authors proposes an uncertainty-aware factor from the variance of statistical differences between the vanilla teacher and Avatars, to adjust Avatars' contribution on knowledge transfer adaptively.
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator
Xiang Gao,Cheng Luo,Qinliang Lin,Weicheng Xie,Minmin Liu,Linlin Shen,Keerthy Kusumam,Siyang Song +7 more
TL;DR: Wang et al. as discussed by the authors proposed a patch quilting generative adversarial network (PQ-GAN) to learn the first scale-free CNN generator that can be applied to attack images with arbitrary scales for various computer vision tasks.
Journal ArticleDOI
Opti-CAM: Optimizing saliency maps for interpretability
TL;DR: Opti-CAM as discussed by the authors combines ideas from CAM-based and masking-based approaches, where weights are optimized per image such that the logit of the masked image for a given class is maximized.
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