Grad-CAM Secrets: How Big Receptive Fields Change Everything
Grad-CAM (Gradient-weighted Class Activation Mapping) has become a go-to technique for peering into the “black box” of deep neural networks, allowing researchers to visualize which parts of an input image most influence a model’s decision. Traditionally, we think of Grad-CAM as a purely local explanation — highlighting regions that directly activate certain neurons. However, the role of receptive field size in these visualizations is often underestimated. The size of a receptive field, which represents how much of the input space a neuron can “see,” fundamentally shapes the way Grad-CAM heatmaps are formed. When receptive fields are small , each neuron responds to fine-grained, localized features — like edges, textures, or small objects. In this case, Grad-CAM visualizations tend to be sharply focused, pinpointing exact areas of interest. This is useful for tasks that depend on small details, such as medical imaging or fine object detection. However, small receptive fields can a...
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