Add to ORKGNeural networks are becoming increasingly better at tasks that involve classifying and recognizing images. At the same time techniques intended to explain the network output have been proposed. One such technique is the Gradient-based Class Activation Map (Grad-CAM), which is able to locate features of an input image at various levels of a convolutional neural network (CNN), but is sensitive to the vanishing gradients problem. There are techniques such as Integrated Gradients (IG), that are not affected by that problem, but its use is limited to the input layer of a network. Here we introduce a new technique to produce visual explanations for the predictions of a CNN. Like Grad-CAM, our method can be applied to any layer of the network, and...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
Zurowietz M, Nattkemper TW. An Interactive Visualization for Feature Localization in Deep Neural Net...
Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have ...
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solvi...
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the outp...
The adoption of deep convolutional neural networks (CNN) is growing exponentially in wide varieties ...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
In this paper, an enhancement technique for the class activation mapping methods such as gradient-we...
Deep neural networks have exhibited state-of-the-art performance in many com- puter vision tasks. H...
Recent years have produced great advances in training large, deep neural networks (DNNs), in-cluding...
Deep neural network models perform well in a variety of domains, such as computer vision, recommende...
In a number of fields, neural networks can achieve state-of-the-art performance, but understanding h...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
Zurowietz M, Nattkemper TW. An Interactive Visualization for Feature Localization in Deep Neural Net...
Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have ...
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solvi...
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the outp...
The adoption of deep convolutional neural networks (CNN) is growing exponentially in wide varieties ...
Deep neural networks are ubiquitous due to the ease of developing models and their influence on othe...
In this paper, an enhancement technique for the class activation mapping methods such as gradient-we...
Deep neural networks have exhibited state-of-the-art performance in many com- puter vision tasks. H...
Recent years have produced great advances in training large, deep neural networks (DNNs), in-cluding...
Deep neural network models perform well in a variety of domains, such as computer vision, recommende...
In a number of fields, neural networks can achieve state-of-the-art performance, but understanding h...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Purpose Existing class activation mapping (CAM) techniques extract the feature maps only from a sing...
Zurowietz M, Nattkemper TW. An Interactive Visualization for Feature Localization in Deep Neural Net...
Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have ...