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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Journal ArticleDOI
Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors
TL;DR: A novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed and it is believed that this method is instructive for addressing the FD problems in other similar fields.
Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training
Yuting He,Rongjun Ge,Guanyu Yang +2 more
TL;DR: In this article , Chen et al. proposed the knowledge boosting contrastive vision-language pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision- language semantic consistency.
Journal ArticleDOI
Knowledge Distillation from Single to Multi Labels: an Empirical Study
TL;DR: In this paper , a novel knowledge distillation method based on Class Activation Maps (CAMs) is proposed for multi-label classification, which is both effective and straightforward to implement.
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