Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds.

Due to their inherent complexity, these tasks pose significant challenges for current machine learning systems.

Segmentation, the task of delineating and isolating distinct objects, is a fundamental problem in computer vision. Much of the current approaches are supervised, relying on expensive manual annotations.

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.

Team-Nash equilibrium (TNE) predicts the outcomes of such coordinated interactions.

We make the comparisons of different NAS methods in terms of the empirical running time.