1 code implementation • 26 Feb 2024 • Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad H. Danesh, Li Fuxin
In the presence of imbalanced multi-class training data, GANs tend to favor classes with more samples, leading to the generation of low-quality and less diverse samples in tail classes.
no code implementations • 13 Dec 2022 • Mingqi Jiang, Saeed Khorram, Li Fuxin
In order to gain insights about the decision-making of different visual recognition backbones, we propose two methodologies, sub-explanation counting and cross-testing, that systematically applies deep explanation algorithms on a dataset-wide basis, and compares the statistics generated from the amount and nature of the explanations.
4 code implementations • 19 Jun 2022 • Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, Yue Zhao
Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
no code implementations • COLING 2018 • Jingjing Wang, Shoushan Li, Mingqi Jiang, Hanqian Wu, Guodong Zhou
In realistic scenarios, a user profiling model (e. g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media.