Live briefing
Everything about Class activation mapping
Explainable AI technique
Class activation mapping methods are explainable AI (XAI) techniques used to visualize the regions of an input image that are the most relevant for a particular task, especially image classification, in convolutional neural networks (CNNs). These methods generate heatmaps by weighting the feature maps from a convolutional layer according to their relevance to the target class. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Class activation mappingA 5-minute orientation
- Skim a recent paper: Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation MappingInfluential work since 2022
- Pick up a classic book: Selbstverwaltung und sozialer Wandel in der russischen ProvinzThe most-reprinted book on the subject
- Read the landmark paper: Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)Cited 148 times
- Go sideways: Convolutional neural networkA closely related topic
- Go sideways: BackpropagationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)
CAMF: An Interpretable Infrared and Visible Image Fusion Network Based on Class Activation Mapping
Explainable Deep Learning Models With Gradient-Weighted Class Activation Mapping for Smart Agriculture
Grad-CAM (Gradient-weighted Class Activation Mapping): A systematic literature review
MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images
Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping
Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping
🆕 Influential since 2022
Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping
CS-WSCDNet: Class Activation Mapping and Segment Anything Model-Based Framework for Weakly Supervised Change Detection
Dual-stage interpretable domain generalization fault diagnosis: integrating prior knowledge and gradient-weighted class activation mapping
AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping
Mixed-UNet: Refined class activation mapping for weakly-supervised semantic segmentation with multi-scale inference
📚 Books worth reading
- 📕Selbstverwaltung und sozialer Wandel in der russischen ProvinzFranziska Schedewie · 2006
- 📕Wie wählte Göttingen?Fritz Hasselhorn · 1983
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Convolutional neural network A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep…
- Backpropagation In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is…
- Multilayer perceptron In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation…
- LeNet LeNet is a series of convolutional neural network architectures created by a research group at AT&T Bell Laboratories between 1988 and 1998, centered around…
- Vanishing gradient problem In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered…
- Residual neural network A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions…
- Types of artificial neural networks Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers, unit…
- Time delay neural network Time delay neural network (TDNN) is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance…