What is Gradient-weighted Class Activation Mapping (Grad-CAM)?

The deep learning models are often called black boxes, making surprisingly accurate predictions. This means that although these models can produce good results, the reasoning and thinking behind what goes inside these models are missing. This raises the question: “Can we understand these black box models?” Grad-CAM is an attempt to visually understand deep learning models, specifically in convolutional neural networks.

Backpropagation is the go-to algorithm for training models. In backpropagation, gradients are continuously updated and backpropagated to the neural network. So, gradients drive the learning part of deep learning. Grad-CAM uses the gradients flowing into the convolutional layers. Using Grad-CAM, we can point out the focus areas of the model and train the model accordingly.

How to use Grad-CAM

Although the basic principle behind the Grad-CAM is to capture the gradients flowing into convolutional layers, using this information properly can lead to better insights. The recommended way of using Grad-CAM is to analyze the gradients flowing into the final convolutional layer.

Advantages

Grad-CAM can be seen as one of the early developments in explainable deep learning. As a technique for explaining deep learning, it comes with the following advantages:

• Standalone: Grad-CAM is a standalone technique that doesn’t involve training or processing the model for explainability.

• Generic: Grad-CAM is a generic technique to evaluate models. This allows a fair comparison of models in the same environment settings.

• Versatile: Grad-CAM is a versatile technique that can be applied to various tasks. This means we do not have to worry about the problem being solved. For example, object detection and image classification are two different tasks, but Grad-CAM can provide explainability for both.

Disadvantages

Here are some of the drawbacks of using Grad-CAM:

• Sensitivity: Grad-CAM assumes that the final layer can represent the information of the previous layers. This is not always true, as the results can be sensitive to the architecture.

• Uncertainty: Grad-CAM uses only the gradients of the model to give explanations and ignores the confidence of those gradients. This means that although we know where the gradients are localized, we do not know the importance of localizations in the model decision. That’s why Grad-CAM lacks the certainty of decisions made by the model.

Conclusion

To sum up, the Grad-CAM is a technique that visualizes the gradients to understand the deep learning models. Although Grad-CAM is a valuable technique for getting insights of the model, it can not always map the gradients accurately to the explanations.