Attention Maps visualize the weightings of attention mechanisms in neural networks, which determine how strongly individual input elements are considered during processing.
These maps show which parts of the input data are particularly important for the model and help to make the decision-making processes more understandable.
In transformer models, for example, attention scores between tokens are calculated, which can be displayed as heatmaps to illustrate the focus on specific words or image regions.
The analysis of attention maps supports debugging and improving models as well as increasing transparency in complex tasks such as machine translation or image captioning.