# Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models

arXiv: https://arxiv.org/abs/1908.01224 · HTML: https://mlsanity.com/paper/1908.01224/

## Key facts

- Cited by 118 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2019.

## Built on by (most cited first, 30 of 118)

- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Boosting Out-of-distribution Detection with Typical Features](https://mlsanity.com/paper/2210.04200/) (2022)
- [Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases](https://mlsanity.com/paper/2303.05470/) (2023)
- [From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks](https://mlsanity.com/paper/2406.06579/) (2024)
- [Gradient based Feature Attribution in Explainable AI: A Technical Review](https://mlsanity.com/paper/2403.10415/) (2024)
- [Token Activation Map to Visually Explain Multimodal LLMs](https://mlsanity.com/paper/2506.23270/) (2025)
- [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://mlsanity.com/paper/2109.07069/) (2021)
- [Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis](https://mlsanity.com/paper/2104.10252/) (2021)
- [Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value](https://mlsanity.com/paper/2208.03608/) (2022)
- [What Makes a Good Explanation?: A Harmonized View of Properties of Explanations](https://mlsanity.com/paper/2211.05667/) (2022)
- [Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks](https://mlsanity.com/paper/2207.03648/) (2022)
- [Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring](https://mlsanity.com/paper/2102.07805/) (2021)
- [Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation](https://mlsanity.com/paper/2509.22496/) (2025)
- [DORA: Exploring Outlier Representations in Deep Neural Networks](https://mlsanity.com/paper/2206.04530/) (2022)
- [TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos](https://mlsanity.com/paper/2208.14542/) (2022)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.13252/) (2021)
- [Re-Attention Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2208.01838/) (2022)
- [Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application](https://mlsanity.com/paper/2301.04829/) (2023)
- [Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better](https://mlsanity.com/paper/2506.12766/) (2025)
- [Certifiably Robust Interpretation via Renyi Differential Privacy](https://mlsanity.com/paper/2107.01561/) (2021)
- [Poly-CAM: High resolution class activation map for convolutional neural networks](https://mlsanity.com/paper/2204.13359/) (2022)
- [Identifying Spurious Correlations and Correcting them with an Explanation-based Learning](https://mlsanity.com/paper/2211.08285/) (2022)
- [Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization](https://mlsanity.com/paper/2507.10846/) (2025)
- [Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity](https://mlsanity.com/paper/2111.05329/) (2021)
- [Negative Evidence Matters in Interpretable Histology Image Classification](https://mlsanity.com/paper/2201.02445/) (2022)
- [What do navigation agents learn about their environment?](https://mlsanity.com/paper/2206.08500/) (2022)
- [Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models](https://mlsanity.com/paper/2303.11969/) (2023)
- [Benchmarking the Attribution Quality of Vision Models](https://mlsanity.com/paper/2407.11910/) (2024)

## Abstract (arXiv)

Gaining insight into how deep convolutional neural network models perform image classification and how to explain their outputs have been a concern to computer vision researchers and decision makers. These deep models are often referred to as black box due to low comprehension of their internal workings. As an effort to developing explainable deep learning models, several methods have been proposed such as finding gradients of class output with respect to input image (sensitivity maps), class activation map (CAM), and Gradient based Class Activation Maps (Grad-CAM). These methods under perform when localizing multiple occurrences of the same class and do not work for all CNNs. In addition, Grad-CAM does not capture the entire object in completeness when used on single object images, this affect performance on recognition tasks. With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ footnoteSimple demo: http://35.238.22.135:5000/, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++. Our Smooth Grad-CAM++ technique provides the capability of either visualizing a layer, subset of feature maps, or subset of neurons within a feature map at each instance at the inference level (model prediction process). After experimenting with few images, Smooth Grad-CAM++ produced more visually sharp maps with better localization of objects in the given input images when compared with other methods.