We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset.

Existing methods typically adopted a prior-based motion model ( e.g ., the local affine motion

Subject screening To gain entry into the study, subjects were required to first perform a "demo" task consisting of 100 We refer to measures of human choice probability that are lapse-rate correct in this manner as "Normalized" (e.g., Supp. The typically observed lapse rates were quite low (median over subjects: 0%; mean 4.9%), indicating Figure 3: Human disruption rates are largely stable across stimulus presentation times. At shorter viewing times, we observed modest or no increases in disruption rate. Source images were captured with a smartphone camera. ImageNet classes, as previously defined in robustness library [2].

The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations.

We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image.

We propose a Level Set Traversal algorithm that iteratively explores regions of high confidence with respect to the input space using orthogonal components of the local gradients.