We know that activation != relevance. To localize concepts in input space, we initialize CRP with channel activations using the start_layer argument of the CondAttribution class. This works well for ActMax and is highly efficient. But for RelMax, the heatmap could be inverted or missing parts. Thus, in a future version of zennit-crp, we will perform localization of RelMax samples with a complete backward pass beginning at the output of the model to utilize the intermediate relevances i.e. the condition set = [{layer: channel, y:class}] and not start_layer argument. This is computationally less efficient, but results in better localization.
We know that activation != relevance. To localize concepts in input space, we initialize CRP with channel activations using the start_layer argument of the CondAttribution class. This works well for ActMax and is highly efficient. But for RelMax, the heatmap could be inverted or missing parts. Thus, in a future version of zennit-crp, we will perform localization of RelMax samples with a complete backward pass beginning at the output of the model to utilize the intermediate relevances i.e. the condition set = [{layer: channel, y:class}] and not start_layer argument. This is computationally less efficient, but results in better localization.