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 $\epsilon$.

**UNIMO** is a multi-modal pre-training architecture that can effectively adapt to both single modal and multimodal understanding and generation tasks. UNIMO learns visual representations and textual representations simultaneously, and unifies them into the same semantic space via [cross-modal contrastive learning](https://paperswithcode.com/method/cmcl) (CMCL) based on a large-scale corpus of image collections, text corpus and image-text pairs. The CMCL aligns the visual representation and textual representation, and unifies them into the same semantic
space based on image-text pairs.

UNIMO

UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Accuracy-Robustness Area (ARA)

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

**Tofu** is an intra-layer model parallel system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators used by platforms like MXNet and TensorFlow. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost.

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