Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that produce a high activation.

The examples that I saw so far are related to classification tasks. So my question is: can these concepts be applied to other convolutional neural network tasks, like semantic segmentation or image embedding (triplet loss)? What can I expect if I apply visualization algorithms to these networks?


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