Add to ORKGThe 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 i...
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made ...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural netw...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
The need for clear, trustworthy explanations of deep learning model predictions is essential for hig...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
Graph convolutional neural network (GCN) has drawn increasing attention and attained good performanc...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
In this paper, an enhancement technique for the class activation mapping methods such as gradient-we...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the outp...
We propose an end-to-end-trainable feature augmentation module built for image classification that e...
Deep learning (DL) models achieve remarkable performance in classification tasks. However, models wi...
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made ...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural netw...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
The need for clear, trustworthy explanations of deep learning model predictions is essential for hig...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
Graph convolutional neural network (GCN) has drawn increasing attention and attained good performanc...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
In this paper, an enhancement technique for the class activation mapping methods such as gradient-we...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the outp...
We propose an end-to-end-trainable feature augmentation module built for image classification that e...
Deep learning (DL) models achieve remarkable performance in classification tasks. However, models wi...
Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made ...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural netw...