In the space of adversarial perturbation against classifier accuracy, the ARA is the area between a classifier's curve and the straight line defined by a naive classifier's maximum accuracy. Intuitively, the ARA measures a combination of the classifier’s predictive power and its ability to overcome an adversary. Importantly, when contrasted against existing robustness metrics, the ARA takes into account the classifier’s performance against all adversarial examples, without  bounding them by some arbitrary $\epsilon$.

**PVT**, or **Pyramid Vision Transformer**, is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) that utilizes a pyramid structure to make it an effective backbone for dense prediction tasks. Specifically it allows for more fine-grained inputs (4 x 4 pixels per patch) to be used, while simultaneously shrinking the sequence length of the Transformer as it deepens - reducing the computational cost. Additionally, a [spatial-reduction attention](https://paperswithcode.com/method/spatial-reduction-attention) (SRA) layer is used to further reduce the resource consumption when learning high-resolution features.

The entire model is divided into four stages, each of which is comprised of a patch embedding layer and a $\mathcal{L}\_{i}$-layer Transformer encoder. Following a pyramid structure, the output resolution of the four stages progressively shrinks from high (4-stride) to low (32-stride).

Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Accuracy-Robustness Area (ARA)

Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness

**Inception-A** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture.

Source	Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com