Weakness: 1.They stack the methods of Mirzasoleiman et al., 2020 and Group-learning setting, and then use one classical method DBSCAN to cluster. 2. In comparison of gradient space and feature space, the normalization of the data in Figure 2 is not so clear. I think you do not need to do normalization of the data, since the shrinkage of the correctly classified points is profit to outlier identification. 3. It is not clear what variable the derivative is based on. I thought the gradient is a very large dimensional vector (tensor). Since the network is with many layers (e.g. ResNet-50), the dimension of the derivative should be about 50 times. When moving to numerical result, I found the ResNet-50 is pretrained and the derivative is only related to the parameters of logistic regression.
1.They stack the methods of Mirzasoleiman et al., 2020 and Group-learning setting, and then use one classical method DBSCAN to cluster.