Add to ORKGThe Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its authors can provide better visual explanations for network predictions, and does a better job at locating objects even for occurrences of multiple object instances in a single image. Here we show that Grad-CAM++ is practically equivalent to a very simple variation of Grad-CAM in which gradients are replaced with positive gradients.Comment: 10 pages, 8 figure
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
The architecture and the parameters of neural networks are often optimized independently, which requ...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solvi...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Gradient-weighted Class Activation Mapping (Grad-CAM) has been a successful technique to produce vis...
One of the fundamental advancements in the deployment of object detectors in real-time applications ...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Convolutional neural networks have become state-of-the-art in a wide range of image recognition task...
We present Augmented Grad-CAM, a general framework to provide a high-resolution visual explanation o...
Machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the pe...
Convolutional neural networks have become state-of-the-art in a wide range of image recognition task...
The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solv...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
The architecture and the parameters of neural networks are often optimized independently, which requ...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solvi...
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing i...
The need for Explainable AI is increasing with the development of deep learning. The saliency maps d...
Gradient-weighted Class Activation Mapping (Grad-CAM) has been a successful technique to produce vis...
One of the fundamental advancements in the deployment of object detectors in real-time applications ...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
Convolutional neural networks have become state-of-the-art in a wide range of image recognition task...
We present Augmented Grad-CAM, a general framework to provide a high-resolution visual explanation o...
Machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the pe...
Convolutional neural networks have become state-of-the-art in a wide range of image recognition task...
The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solv...
Recent research in deep learning methodology has led to a variety of complex modelling techniques in...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural...
The architecture and the parameters of neural networks are often optimized independently, which requ...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...