Is Error similarity score equal to TracIN score in colab notebooks?

[VenkateshSatagopan]

Hi Frederick,
Thanks for your colab notebook implementation. I am trying to understand the calculation of TracIN score in the notebook "resnet50_imagenet_proponents_opponents" where you calculate 3 scores in the loss function as shown below

def find(loss_grad=None, activation=None, topk=50):
        if loss_grad is None and activation is None:
              raise ValueError('loss grad and activation cannot both be None.')
        scores = []
        scores_lg = []
        scores_a = []
        for i in range(len(trackin_train['image_ids'])):
             if loss_grad is not None and activation is not None:
                     lg_sim = np.sum(trackin_train['loss_grads'][i] * loss_grad)
                     a_sim = np.sum(trackin_train['activations'][i] * activation)
                     scores.append(lg_sim * a_sim)
                     scores_lg.append(lg_sim)
                     scores_a.append(a_sim)

Here you calculate lg_sim, a_sim, and scores and mention them as error_similarity, encoding similarity and influence when you display proponents and opponents for a particular test image.
lg_sim calculation is similar to the formula for calculating TracIN mentioned in the paper. so is the lg_sim score is equivalent to TracIN scores for differentiating proponents and opponents? Is my understanding correct? If so what are the significance of a_sim and scores parameters?

Thanks in Advance

[frederick0329]

Thank you for the question.

Please refer to appendix F of the paper.
influence(tracin score) = lg_sim (error_similarity) * a_sim (encoding similarity)

[VenkateshSatagopan]

Thanks frederick,
I got it now about the influence score calculation.