  The different families of saliency methods, either based on contrastive
signals, closed-form formulas mixing gradients with activations or on
perturbation masks, all focus on which parts of an image are responsible for
the model's inference. In this paper, we are rather interested by the locations
of an image that contribute to the model's training. First, we propose a
principled attribution method that we extract from the summation formula used
to compute the gradient of the weights for a 1x1 convolutional layer. The
resulting formula is fast to compute and can used throughout the network,
allowing us to efficiently produce fined-grained importance maps. We will show
how to extend it in order to compute saliency maps at any targeted point within
the network. Secondly, to make the attribution really specific to the training
of the model, we introduce a meta-learning approach for saliency methods by
considering an inner optimisation step within the loss. This way, we do not aim
at identifying the parts of an image that contribute to the model's output but
rather the locations that are responsible for the good training of the model on
this image. Conversely, we also show that a similar meta-learning approach can
be used to extract the adversarial locations which can lead to the degradation
of the model.
